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GEM-SciDuet-train-1#paper-954#slide-0 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-0 | Syntax in Statistical Machine Translation | Translation Model vs Language Model
Syntactic LM Decoder Integration Results Questions? | Translation Model vs Language Model
Syntactic LM Decoder Integration Results Questions? | [] |
GEM-SciDuet-train-1#paper-954#slide-1 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-1 | Syntax in the Language Model | Translation Model vs Language Model
Syntactic LM Decoder Integration Results Questions?
An incremental syntactic language model uses an incremental statistical parser to define a probability model over the dependency or phrase structure of target language strings.
Phrase-based decoder produces translation in the target... | Translation Model vs Language Model
Syntactic LM Decoder Integration Results Questions?
An incremental syntactic language model uses an incremental statistical parser to define a probability model over the dependency or phrase structure of target language strings.
Phrase-based decoder produces translation in the target... | [] |
GEM-SciDuet-train-1#paper-954#slide-2 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-2 | Incremental Parsing | DT NN VP PP
The president VB NP IN NP
meets DT NN on Friday NP/NN NN VP/NP DT board
Motivation Decoder Integration Results Questions?
the president VB NP VP/NN
Transform right-expanding sequences of constituents into left-expanding sequences of incomplete constituents
NP VP S/NP NP
the board DT president VB the
Incompl... | DT NN VP PP
The president VB NP IN NP
meets DT NN on Friday NP/NN NN VP/NP DT board
Motivation Decoder Integration Results Questions?
the president VB NP VP/NN
Transform right-expanding sequences of constituents into left-expanding sequences of incomplete constituents
NP VP S/NP NP
the board DT president VB the
Incompl... | [] |
GEM-SciDuet-train-1#paper-954#slide-3 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-3 | Incremental Parsing using HHMM Schuler et al 2010 | Hierarchical Hidden Markov Model
Circles denote hidden random variables
Edges denote conditional dependencies
NP/NN NN VP/NP DT board
Isomorphic Tree Path DT president VB the
Shaded circles denote observed values
Motivation Decoder Integration Results Questions?
Analogous to Maximally Incremental
e1 =The e2 =president ... | Hierarchical Hidden Markov Model
Circles denote hidden random variables
Edges denote conditional dependencies
NP/NN NN VP/NP DT board
Isomorphic Tree Path DT president VB the
Shaded circles denote observed values
Motivation Decoder Integration Results Questions?
Analogous to Maximally Incremental
e1 =The e2 =president ... | [] |
GEM-SciDuet-train-1#paper-954#slide-4 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-4 | Phrase Based Translation | Der Prasident trifft am Freitag den Vorstand
The president meets the board on Friday
s president president Friday
s that that president Obama met
AAAAAA EAAAAA EEAAAA EEIAAA
s s the the president president meets
Stack Stack Stack Stack
Motivation Syntactic LM Results Questions? | Der Prasident trifft am Freitag den Vorstand
The president meets the board on Friday
s president president Friday
s that that president Obama met
AAAAAA EAAAAA EEAAAA EEIAAA
s s the the president president meets
Stack Stack Stack Stack
Motivation Syntactic LM Results Questions? | [] |
GEM-SciDuet-train-1#paper-954#slide-5 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-5 | Phrase Based Translation with Syntactic LM | represents parses of the partial translation at node h in stack t
s president president Friday
s that that president Obama met
AAAAAA EAAAAA EEAAAA EEIAAA
s s the the president president meets
Stack Stack Stack Stack
Motivation Syntactic LM Results Questions? | represents parses of the partial translation at node h in stack t
s president president Friday
s that that president Obama met
AAAAAA EAAAAA EEAAAA EEIAAA
s s the the president president meets
Stack Stack Stack Stack
Motivation Syntactic LM Results Questions? | [] |
GEM-SciDuet-train-1#paper-954#slide-6 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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s the the president president meets meets the
Motivation Syntactic LM Results Questions?
president meets the board | EAAAAA EEAAAA EEIAAA EEIIAA
s the the president president meets meets the
Motivation Syntactic LM Results Questions?
president meets the board | [] |
GEM-SciDuet-train-1#paper-954#slide-7 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-7 | Direct Maximum Entropy Model of Translation | e argmax exp jhj(e,f)
h Distortion model n-gram LM
Set of j feature weights
Syntactic LM P( th)
AAAAAA EAAAAA EEAAAA EEIAAA
s s the the president president meets
Stack Stack Stack Stack
Motivation Syntactic LM Results Questions? | e argmax exp jhj(e,f)
h Distortion model n-gram LM
Set of j feature weights
Syntactic LM P( th)
AAAAAA EAAAAA EEAAAA EEIAAA
s s the the president president meets
Stack Stack Stack Stack
Motivation Syntactic LM Results Questions? | [] |
GEM-SciDuet-train-1#paper-954#slide-8 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-8 | Does an Incremental Syntactic LM Help Translation | but will it make my BLEU score go up?
Motivation Syntactic LM Decoder Integration Questions?
Moses with LM(s) BLEU
Using n-gram LM only
Using n-gram LM + Syntactic LM
NIST OpenMT 2008 Urdu-English data set
Moses with standard phrase-based translation model
Tuning and testing restricted to sentences 20 words long
Result... | but will it make my BLEU score go up?
Motivation Syntactic LM Decoder Integration Questions?
Moses with LM(s) BLEU
Using n-gram LM only
Using n-gram LM + Syntactic LM
NIST OpenMT 2008 Urdu-English data set
Moses with standard phrase-based translation model
Tuning and testing restricted to sentences 20 words long
Result... | [] |
GEM-SciDuet-train-1#paper-954#slide-9 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-9 | Perplexity Results | Language models trained on WSJ Treebank corpus
Motivation Syntactic LM Decoder Integration Questions?
WSJ 5-gram + WSJ SynLM
...and n-gram model for larger English Gigaword corpus.
Gigaword 5-gram + WSJ SynLM | Language models trained on WSJ Treebank corpus
Motivation Syntactic LM Decoder Integration Questions?
WSJ 5-gram + WSJ SynLM
...and n-gram model for larger English Gigaword corpus.
Gigaword 5-gram + WSJ SynLM | [] |
GEM-SciDuet-train-1#paper-954#slide-10 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-10 | Summary | Straightforward general framework for incorporating any
Incremental Syntactic LM into Phrase-based Translation
We used an Incremental HHMM Parser as Syntactic LM
Syntactic LM shows substantial decrease in perplexity on out-of-domain data over n-gram LM when trained on same data
Syntactic LM interpolated with n-gram LM ... | Straightforward general framework for incorporating any
Incremental Syntactic LM into Phrase-based Translation
We used an Incremental HHMM Parser as Syntactic LM
Syntactic LM shows substantial decrease in perplexity on out-of-domain data over n-gram LM when trained on same data
Syntactic LM interpolated with n-gram LM ... | [] |
GEM-SciDuet-train-1#paper-954#slide-11 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-11 | This looks a lot like CCG | Our parser performs some CCG-style operations:
Type raising in conjunction with forward function composition
Motivation Syntactic LM Decoder Integration Results | Our parser performs some CCG-style operations:
Type raising in conjunction with forward function composition
Motivation Syntactic LM Decoder Integration Results | [] |
GEM-SciDuet-train-1#paper-954#slide-12 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-12 | Why not just use CCG | No probablistic version of incremental CCG
Our parser is constrained
(we dont have backward composition)
We do use those components of CCG (forward function application and forward function composition) which are useful for probabilistic incremental parsing
Motivation Syntactic LM Decoder Integration Results | No probablistic version of incremental CCG
Our parser is constrained
(we dont have backward composition)
We do use those components of CCG (forward function application and forward function composition) which are useful for probabilistic incremental parsing
Motivation Syntactic LM Decoder Integration Results | [] |
GEM-SciDuet-train-1#paper-954#slide-13 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-13 | Speed Results | Mean per-sentence decoding time
Parser beam sizes are indicated for the syntactic LM
Parser runs in linear time, but were parsing all paths through the Moses lattice as they are generated by the decoder
More informed pruning, but slower decoding
Motivation Syntactic LM Decoder Integration Results | Mean per-sentence decoding time
Parser beam sizes are indicated for the syntactic LM
Parser runs in linear time, but were parsing all paths through the Moses lattice as they are generated by the decoder
More informed pruning, but slower decoding
Motivation Syntactic LM Decoder Integration Results | [] |
GEM-SciDuet-train-1#paper-954#slide-14 | 954 | Incremental Syntactic Language Models for Phrase-based Translation | This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. Bottom-up and topdown parsers typically require a completed string as input. This requirement makes it difficult to incorporate them into phrase-based translation, whi... | {
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"Incorporating a Syntactic Language Mod... | GEM-SciDuet-train-1#paper-954#slide-14 | Phrase Based Translation w ntactic | e string of n target language words e1. . .en
et the first t words in e, where tn
t set of all incremental parses of et
def t subset of parses t that remain after parser pruning
e argmax P( e) t1 t
Motivation Syntactic LM Decoder Integration Results | e string of n target language words e1. . .en
et the first t words in e, where tn
t set of all incremental parses of et
def t subset of parses t that remain after parser pruning
e argmax P( e) t1 t
Motivation Syntactic LM Decoder Integration Results | [] |
GEM-SciDuet-train-2#paper-957#slide-0 | 957 | LINA: Identifying Comparable Documents from Wikipedia | This paper describes the LINA system for the BUCC 2015 shared track. Following (Enright and Kondrak, 2007), our system identify comparable documents by collecting counts of hapax words. We extend this method by filtering out document pairs sharing target documents using pigeonhole reasoning and cross-lingual informatio... | {
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} | GEM-SciDuet-train-2#paper-957#slide-0 | Introduction | I How far can we go with a language agnostic model?
I We experiment with [Enright and Kondrak, 2007]s parallel document identification
I We adapt the method to the BUCC-2015 Shared task based on two assumptions:
Source documents should be paired 1-to-1 with target documents
We have access to comparable documents in sev... | I How far can we go with a language agnostic model?
I We experiment with [Enright and Kondrak, 2007]s parallel document identification
I We adapt the method to the BUCC-2015 Shared task based on two assumptions:
Source documents should be paired 1-to-1 with target documents
We have access to comparable documents in sev... | [] |
GEM-SciDuet-train-2#paper-957#slide-1 | 957 | LINA: Identifying Comparable Documents from Wikipedia | This paper describes the LINA system for the BUCC 2015 shared track. Following (Enright and Kondrak, 2007), our system identify comparable documents by collecting counts of hapax words. We extend this method by filtering out document pairs sharing target documents using pigeonhole reasoning and cross-lingual informatio... | {
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} | GEM-SciDuet-train-2#paper-957#slide-1 | Method | I Fast parallel document identification [Enright and Kondrak, 2007]
I Documents = bags of hapax words
I Words = blank separated strings that are 4+ characters long
I Given a document in language A, the document in language B that shares the largest
number of words is considered as parallel
I Works very well for paralle... | I Fast parallel document identification [Enright and Kondrak, 2007]
I Documents = bags of hapax words
I Words = blank separated strings that are 4+ characters long
I Given a document in language A, the document in language B that shares the largest
number of words is considered as parallel
I Works very well for paralle... | [] |
GEM-SciDuet-train-2#paper-957#slide-2 | 957 | LINA: Identifying Comparable Documents from Wikipedia | This paper describes the LINA system for the BUCC 2015 shared track. Following (Enright and Kondrak, 2007), our system identify comparable documents by collecting counts of hapax words. We extend this method by filtering out document pairs sharing target documents using pigeonhole reasoning and cross-lingual informatio... | {
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} | GEM-SciDuet-train-2#paper-957#slide-2 | Improvements using 1 to 1 alignments | I In baseline, document pairs are scored independently
I Multiple source documents are paired to a same target document
I 60% of English pages are paired with multiple pages in French or German
I We remove multiply assigned source documents using pigeonhole reasoning
I From 60% to 11% of multiply assigned source docume... | I In baseline, document pairs are scored independently
I Multiple source documents are paired to a same target document
I 60% of English pages are paired with multiple pages in French or German
I We remove multiply assigned source documents using pigeonhole reasoning
I From 60% to 11% of multiply assigned source docume... | [] |
GEM-SciDuet-train-2#paper-957#slide-3 | 957 | LINA: Identifying Comparable Documents from Wikipedia | This paper describes the LINA system for the BUCC 2015 shared track. Following (Enright and Kondrak, 2007), our system identify comparable documents by collecting counts of hapax words. We extend this method by filtering out document pairs sharing target documents using pigeonhole reasoning and cross-lingual informatio... | {
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"Discussion"
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} | GEM-SciDuet-train-2#paper-957#slide-3 | Improvements using cross lingual information | I Simple document weighting function score ties
I We break the remaining score ties using a third language
I From 11% to less than 4% of multiply assigned source documents | I Simple document weighting function score ties
I We break the remaining score ties using a third language
I From 11% to less than 4% of multiply assigned source documents | [] |
GEM-SciDuet-train-2#paper-957#slide-4 | 957 | LINA: Identifying Comparable Documents from Wikipedia | This paper describes the LINA system for the BUCC 2015 shared track. Following (Enright and Kondrak, 2007), our system identify comparable documents by collecting counts of hapax words. We extend this method by filtering out document pairs sharing target documents using pigeonhole reasoning and cross-lingual informatio... | {
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} | GEM-SciDuet-train-2#paper-957#slide-4 | Experimental settings | I We focus on the French-English and German-English pairs
I The following measures are considered relevant
I Mean Average Precision (MAP) | I We focus on the French-English and German-English pairs
I The following measures are considered relevant
I Mean Average Precision (MAP) | [] |
GEM-SciDuet-train-2#paper-957#slide-5 | 957 | LINA: Identifying Comparable Documents from Wikipedia | This paper describes the LINA system for the BUCC 2015 shared track. Following (Enright and Kondrak, 2007), our system identify comparable documents by collecting counts of hapax words. We extend this method by filtering out document pairs sharing target documents using pigeonhole reasoning and cross-lingual informatio... | {
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} | GEM-SciDuet-train-2#paper-957#slide-5 | Results FR EN | Strategy MAP Succ. P@5 MAP Succ. P@5 | Strategy MAP Succ. P@5 MAP Succ. P@5 | [] |
GEM-SciDuet-train-2#paper-957#slide-6 | 957 | LINA: Identifying Comparable Documents from Wikipedia | This paper describes the LINA system for the BUCC 2015 shared track. Following (Enright and Kondrak, 2007), our system identify comparable documents by collecting counts of hapax words. We extend this method by filtering out document pairs sharing target documents using pigeonhole reasoning and cross-lingual informatio... | {
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} | GEM-SciDuet-train-2#paper-957#slide-6 | Results DE EN | Strategy MAP Succ. P@5 MAP Succ. P@5 | Strategy MAP Succ. P@5 MAP Succ. P@5 | [] |
GEM-SciDuet-train-2#paper-957#slide-7 | 957 | LINA: Identifying Comparable Documents from Wikipedia | This paper describes the LINA system for the BUCC 2015 shared track. Following (Enright and Kondrak, 2007), our system identify comparable documents by collecting counts of hapax words. We extend this method by filtering out document pairs sharing target documents using pigeonhole reasoning and cross-lingual informatio... | {
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} | GEM-SciDuet-train-2#paper-957#slide-7 | Summary | I Unsupervised, hapax words-based method
I Promising results, about 60% of success using pigeonhole reasoning
I Using a third language slightly improves the performance
I Finding the optimal alignment across the all languages
I Relaxing the hapax-words constraint | I Unsupervised, hapax words-based method
I Promising results, about 60% of success using pigeonhole reasoning
I Using a third language slightly improves the performance
I Finding the optimal alignment across the all languages
I Relaxing the hapax-words constraint | [] |
GEM-SciDuet-train-3#paper-964#slide-0 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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"Learning Sentence Representations from Aut... | GEM-SciDuet-train-3#paper-964#slide-0 | Sentence Representation in Conversations | Traditional System: hand-crafted semantic frame
Not scalable to complex domains
Neural dialog models: continuous hidden vectors
Directly output system responses in words
Hard to interpret & control
[Ritter et al 2011, Vinyals et al | Traditional System: hand-crafted semantic frame
Not scalable to complex domains
Neural dialog models: continuous hidden vectors
Directly output system responses in words
Hard to interpret & control
[Ritter et al 2011, Vinyals et al | [] |
GEM-SciDuet-train-3#paper-964#slide-1 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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"Learning Sentence Representations from Aut... | GEM-SciDuet-train-3#paper-964#slide-1 | Why discrete sentence representation | 1. Inrepteablity & controbility & multimodal distribution
2. Semi-supervised Learning [Kingma et al 2014 NIPS, Zhou et al 2017 ACL]
3. Reinforcement Learning [Wen et al 2017]
X = What time do you want to travel?
Model Z1Z2Z3 Encoder Decoder | 1. Inrepteablity & controbility & multimodal distribution
2. Semi-supervised Learning [Kingma et al 2014 NIPS, Zhou et al 2017 ACL]
3. Reinforcement Learning [Wen et al 2017]
X = What time do you want to travel?
Model Z1Z2Z3 Encoder Decoder | [] |
GEM-SciDuet-train-3#paper-964#slide-2 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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"Learning Sentence Representations from Aut... | GEM-SciDuet-train-3#paper-964#slide-2 | Baseline Discrete Variational Autoencoder VAE | M discrete K-way latent variables z with RNN recognition & generation network.
Reparametrization using Gumbel-Softmax [Jang et al., 2016; Maddison et al., 2016]
M discrete K-way latent variables z with GRU encoder & decoder.
FAIL to learn meaningful z because of posterior collapse (z is constant regardless of x)
MANY p... | M discrete K-way latent variables z with RNN recognition & generation network.
Reparametrization using Gumbel-Softmax [Jang et al., 2016; Maddison et al., 2016]
M discrete K-way latent variables z with GRU encoder & decoder.
FAIL to learn meaningful z because of posterior collapse (z is constant regardless of x)
MANY p... | [] |
GEM-SciDuet-train-3#paper-964#slide-3 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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"Learning Sentence Representations from Aut... | GEM-SciDuet-train-3#paper-964#slide-3 | Anti Info Nature in Evidence Lower Bound ELBO | Write ELBO as an expectation over the whole dataset
Expand the KL term, and plug back in:
Minimize I(Z, X) to 0
Posterior collapse with powerful decoder. | Write ELBO as an expectation over the whole dataset
Expand the KL term, and plug back in:
Minimize I(Z, X) to 0
Posterior collapse with powerful decoder. | [] |
GEM-SciDuet-train-3#paper-964#slide-4 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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"Learning Sentence Representations from Aut... | GEM-SciDuet-train-3#paper-964#slide-4 | Discrete Information VAE DI VAE | A natural solution is to maximize both data log likelihood & mutual information.
Match prior result for continuous VAE. [Mazhazni et al 2015, Kim et al 2017]
Propose Batch Prior Regularization (BPR) to minimize KL [q(z)||p(z)] for discrete latent
Fundamentally different from KL-annealing, since | A natural solution is to maximize both data log likelihood & mutual information.
Match prior result for continuous VAE. [Mazhazni et al 2015, Kim et al 2017]
Propose Batch Prior Regularization (BPR) to minimize KL [q(z)||p(z)] for discrete latent
Fundamentally different from KL-annealing, since | [] |
GEM-SciDuet-train-3#paper-964#slide-5 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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"Learning Sentence Representations from Aut... | GEM-SciDuet-train-3#paper-964#slide-5 | Learning from Context Predicting DI VST | Skip-Thought (ST) is well-known distributional sentence representation [Hill et al 2016]
The meaning of sentences in dialogs is highly contextual, e.g. dialog acts.
We extend DI-VAE to Discrete Information Variational Skip Thought (DI-VST). | Skip-Thought (ST) is well-known distributional sentence representation [Hill et al 2016]
The meaning of sentences in dialogs is highly contextual, e.g. dialog acts.
We extend DI-VAE to Discrete Information Variational Skip Thought (DI-VST). | [] |
GEM-SciDuet-train-3#paper-964#slide-6 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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"Learning Sentence Representations from Aut... | GEM-SciDuet-train-3#paper-964#slide-6 | Integration with Encoder Decoders | Policy Network z P(z|c)
Recognition Network z Generator
Optional: penalize decoder if generated x not exhibiting z [Hu et al 2017] | Policy Network z P(z|c)
Recognition Network z Generator
Optional: penalize decoder if generated x not exhibiting z [Hu et al 2017] | [] |
GEM-SciDuet-train-3#paper-964#slide-7 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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Stanford Multi-domain Dialog Dataset (SMD) [Eric and Manning 2017]
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Switchboard (SW) [Jurafsky et al 1997]
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Stanford Multi-domain Dialog Dataset (SMD) [Eric and Manning 2017]
a. 3,031 Human-Woz dialog dataset from 3 domains: weather, navigation & scheduling.
Switchboard (SW) [Jurafsky et al 1997]
a. 2,400 human-human telephone non-task-oriented dialogues about a giv... | [] |
GEM-SciDuet-train-3#paper-964#slide-8 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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DVAE: Discrete VAE with ELBO loss
DI-VAE: Discrete VAE + BPR
DST: Skip thought + Gumbel Softmax
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DVAE: Discrete VAE with ELBO loss
DI-VAE: Discrete VAE + BPR
DST: Skip thought + Gumbel Softmax
DI-VST: Variational Skip Thought + BPR Table 1: Results for various discrete sentence representations. | [] |
GEM-SciDuet-train-3#paper-964#slide-9 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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A large batch size leads to
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I(x,z) is not the final goal | [] |
GEM-SciDuet-train-3#paper-964#slide-11 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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More error-prone since harder to predict
Utterance used in the similar context
Easier to get agreement. | DI-VAE cluster utterances based on the
More error-prone since harder to predict
Utterance used in the similar context
Easier to get agreement. | [] |
GEM-SciDuet-train-3#paper-964#slide-12 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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Automatic Evaluation on SW & DD
Compare latent actions with
The higher the more correlated
Human Evaluation on SMD
Expert look at 5 examples and give a
name to the latent actions
5 workers look at the expert name and
Select the ones that... | M=3, K=5. The trained R will map any utterance into a1 -a2 -a3 . E.g. How are you?
Automatic Evaluation on SW & DD
Compare latent actions with
The higher the more correlated
Human Evaluation on SMD
Expert look at 5 examples and give a
name to the latent actions
5 workers look at the expert name and
Select the ones that... | [] |
GEM-SciDuet-train-3#paper-964#slide-13 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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complexity of the domain.
Usr > Sys & Chat > Task
Predict latent actions from DI-VAE is harder
than the ones from DI-VST
Two types of latent actions has their own
pros & cons. Which one is better is | Provide useful measure about the
complexity of the domain.
Usr > Sys & Chat > Task
Predict latent actions from DI-VAE is harder
than the ones from DI-VST
Two types of latent actions has their own
pros & cons. Which one is better is | [] |
GEM-SciDuet-train-3#paper-964#slide-14 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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First time, a neural dialog system | Examples of interpretable dialog
First time, a neural dialog system | [] |
GEM-SciDuet-train-3#paper-964#slide-15 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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DI-VAE and DI-VST for learning rich sentence latent representation and integration
Learn better context-based latent actions
Encode human knowledge into the learning process.
Learn structured latent action space for complex domains.
Evalua... | An analysis of ELBO that explains the posterior collapse issue for sentence VAE.
DI-VAE and DI-VST for learning rich sentence latent representation and integration
Learn better context-based latent actions
Encode human knowledge into the learning process.
Learn structured latent action space for complex domains.
Evalua... | [] |
GEM-SciDuet-train-3#paper-964#slide-16 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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predict the latent action z based on the
Report accuracy by comparing z and z.
DI-VAE has higher consistency than DI-VST
L helps more in complex domain attr
L helps DI-VST more than DI-VAE attr
DI-VST is not directly helping generating x
ST-ED doesnt work well on SW due to... | Use the recognition network as a classifier to
predict the latent action z based on the
Report accuracy by comparing z and z.
DI-VAE has higher consistency than DI-VST
L helps more in complex domain attr
L helps DI-VST more than DI-VAE attr
DI-VST is not directly helping generating x
ST-ED doesnt work well on SW due to... | [] |
GEM-SciDuet-train-3#paper-964#slide-17 | 964 | Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence ... | {
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an utterance (sentence) X. Latent action is denoted as Z.
Z should capture salient sentence-level features about the response X.
The meaning of latent symbols Z should be independent of the context C.
If meaning of Z depend... | Definition: Latent action is a set of discrete variable that define the high-level attributes of
an utterance (sentence) X. Latent action is denoted as Z.
Z should capture salient sentence-level features about the response X.
The meaning of latent symbols Z should be independent of the context C.
If meaning of Z depend... | [] |
GEM-SciDuet-train-4#paper-965#slide-0 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-0 | Lemmatization | INST ar celu ar celiem
Latvian: cels (English: road) | INST ar celu ar celiem
Latvian: cels (English: road) | [] |
GEM-SciDuet-train-4#paper-965#slide-1 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-1 | Previous work | sentence context helps to lemmatize
ambiguous and unseen words
Bergmanis and Goldwater, 2018 | sentence context helps to lemmatize
ambiguous and unseen words
Bergmanis and Goldwater, 2018 | [] |
GEM-SciDuet-train-4#paper-965#slide-2 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-2 | Ambiguous words | A cels (road): NOUN, sing., ACC
B celis (knee): NOUN, plur., DAT | A cels (road): NOUN, sing., ACC
B celis (knee): NOUN, plur., DAT | [] |
GEM-SciDuet-train-4#paper-965#slide-3 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-3 | Learning from sentences | Lemma annotated sentences are scarce for low resource languages annotating sentences is slow
N types > N (contiguous) tokens | Lemma annotated sentences are scarce for low resource languages annotating sentences is slow
N types > N (contiguous) tokens | [] |
GEM-SciDuet-train-4#paper-965#slide-4 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-4 | N types N tokens | Training on 1k UDT tokens/types | Training on 1k UDT tokens/types | [] |
GEM-SciDuet-train-4#paper-965#slide-5 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-5 | Types in context | algorithms get smarter computers faster
Bergmanis and Goldwater, 2018 | algorithms get smarter computers faster
Bergmanis and Goldwater, 2018 | [] |
GEM-SciDuet-train-4#paper-965#slide-6 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-6 | Proposal Data Augmentation | ...to get types in context | ...to get types in context | [] |
GEM-SciDuet-train-4#paper-965#slide-7 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-7 | Method Data Augmentation | Inflection cels cela N;LOC;SG
Dzives pedeja cela pavadot musu cels
Context cels cela N;LOC;SG
Lemma cels cela N;LOC;SG | Inflection cels cela N;LOC;SG
Dzives pedeja cela pavadot musu cels
Context cels cela N;LOC;SG
Lemma cels cela N;LOC;SG | [] |
GEM-SciDuet-train-4#paper-965#slide-8 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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... | {
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"1",
"2.1",
"3",
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"Introduction",
"Data Augmentation",
"Experimental Setup",
"Conclusion"
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} | GEM-SciDuet-train-4#paper-965#slide-8 | Inflection Tables | INST ar celu ar celiem
Latvian: cels (English: road)
ACC celu celiem celus
celt (build) celot (travel) celis (knee) | INST ar celu ar celiem
Latvian: cels (English: road)
ACC celu celiem celus
celt (build) celot (travel) celis (knee) | [] |
GEM-SciDuet-train-4#paper-965#slide-9 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-9 | Key question | If ambiguous words enforce the use of context:
Is context still useful in the absence of ambiguous forms? | If ambiguous words enforce the use of context:
Is context still useful in the absence of ambiguous forms? | [] |
GEM-SciDuet-train-4#paper-965#slide-10 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-10 | Experiments | Train: 1k types from universal dependency corpus
UniMorph in Wikipedia contexts
Estonian, Finnish, Latvian, Polish,
Romanian, Russian, Swedish, Turkish
Metric: type level macro average accuracy
Test: on standard splits of universal dependency corpus | Train: 1k types from universal dependency corpus
UniMorph in Wikipedia contexts
Estonian, Finnish, Latvian, Polish,
Romanian, Russian, Swedish, Turkish
Metric: type level macro average accuracy
Test: on standard splits of universal dependency corpus | [] |
GEM-SciDuet-train-4#paper-965#slide-12 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-12 | Does model learn from context | context vs no context | context vs no context | [] |
GEM-SciDuet-train-4#paper-965#slide-13 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-13 | Afix ambiguity wuger | Lemma depends on context:
A if wuger is adjective then lemma could be wug
B if wuger is noun then lemma could be wuger | Lemma depends on context:
A if wuger is adjective then lemma could be wug
B if wuger is noun then lemma could be wuger | [] |
GEM-SciDuet-train-4#paper-965#slide-14 | 965 | Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text | Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in lowresource... | {
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} | GEM-SciDuet-train-4#paper-965#slide-14 | Takeaways conclusions | Despite biased data and divergent lemmatization standards
Type based data augmentation helps
Even without the ambiguous types that enforce the use of context
Model use context to disambiguate affixes of unseen words | Despite biased data and divergent lemmatization standards
Type based data augmentation helps
Even without the ambiguous types that enforce the use of context
Model use context to disambiguate affixes of unseen words | [] |
GEM-SciDuet-train-5#paper-966#slide-0 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Introduction",
"Two-stage Deep Neural Network for AES",
"Overview",
"Building Blocks",
"Objective and Training",
"Results and Analyzes",
"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-0 | What is Automated Essay Scoring AES | Computer produces summative assessment for evaluation
Aim: reduce human workload
AES has been put into practical use by ETS from 1999 | Computer produces summative assessment for evaluation
Aim: reduce human workload
AES has been put into practical use by ETS from 1999 | [] |
GEM-SciDuet-train-5#paper-966#slide-1 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Introduction",
"Two-stage Deep Neural Network for AES",
"Overview",
"Building Blocks",
"Objective and Training",
"Results and Analyzes",
"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-1 | Prompt specific and Independent AES | Most existing AES approaches are prompt-specific
Require human labels for each prompt to train
Can achieve satisfying human-machine agreement
Prompt-independent AES remains a challenge
Only non-target human labels are available | Most existing AES approaches are prompt-specific
Require human labels for each prompt to train
Can achieve satisfying human-machine agreement
Prompt-independent AES remains a challenge
Only non-target human labels are available | [] |
GEM-SciDuet-train-5#paper-966#slide-2 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Introduction",
"Two-stage Deep Neural Network for AES",
"Overview",
"Building Blocks",
"Objective and Training",
"Results and Analyzes",
"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-2 | Challenges in Prompt independent AES | Source Prompts Target Prompt
Learn essays Predict target
Previous approaches learn on source prompts
Domain adaption [Phandi et al. EMNLP 2015] Cross-domain learning [Dong & Zhang, EMNLP
Achieved Avg. QWK = 0.6395 at best with up to 100 labeled target essays
Off-topic: essays written for source prompts are mostly irrel... | Source Prompts Target Prompt
Learn essays Predict target
Previous approaches learn on source prompts
Domain adaption [Phandi et al. EMNLP 2015] Cross-domain learning [Dong & Zhang, EMNLP
Achieved Avg. QWK = 0.6395 at best with up to 100 labeled target essays
Off-topic: essays written for source prompts are mostly irrel... | [] |
GEM-SciDuet-train-5#paper-966#slide-3 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Overview",
"Building Blocks",
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-3 | TDNN A Two stage Deep Neural Network for Prompt | Based on the idea of transductive transfer learning
Learn on target essays
Utilize the content of target essays to rate | Based on the idea of transductive transfer learning
Learn on target essays
Utilize the content of target essays to rate | [] |
GEM-SciDuet-train-5#paper-966#slide-4 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-4 | The Two stage Architecture | Prompt-independent stage: train a shallow model to create pseudo labels on the target prompt
Prompt-dependent stage: learn an end-to-end model to predict essay ratings for the target prompts | Prompt-independent stage: train a shallow model to create pseudo labels on the target prompt
Prompt-dependent stage: learn an end-to-end model to predict essay ratings for the target prompts | [] |
GEM-SciDuet-train-5#paper-966#slide-5 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-5 | Prompt independent stage | Train a robust prompt-independent AES model
Learning algorithm: RankSVM for AES
Select confident essays written for the target prompt
Predicted ratings in as negative examples
Predicted ratings in as positive examples
Converted to 0/1 labels
Common sense: 8 is good, <5 is bad | Train a robust prompt-independent AES model
Learning algorithm: RankSVM for AES
Select confident essays written for the target prompt
Predicted ratings in as negative examples
Predicted ratings in as positive examples
Converted to 0/1 labels
Common sense: 8 is good, <5 is bad | [] |
GEM-SciDuet-train-5#paper-966#slide-6 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-6 | Prompt dependent stage | Train a hybrid deep model for a prompt-
An end-to-end neural network with three parts | Train a hybrid deep model for a prompt-
An end-to-end neural network with three parts | [] |
GEM-SciDuet-train-5#paper-966#slide-7 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-7 | Architecture of the hybrid deep model | Multi-layer structure: Words (phrases) - Sentences Essay | Multi-layer structure: Words (phrases) - Sentences Essay | [] |
GEM-SciDuet-train-5#paper-966#slide-8 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Two-stage Deep Neural Network for AES",
"Overview",
"Building Blocks",
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"Results and Analyzes",
"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-8 | Model Training | Training loss: MSE on 0/1 pseudo labels
Validation metric: Kappa on 30% non-target essays
Select the model that can best rate | Training loss: MSE on 0/1 pseudo labels
Validation metric: Kappa on 30% non-target essays
Select the model that can best rate | [] |
GEM-SciDuet-train-5#paper-966#slide-9 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-9 | Dataset and Metrics | We use the standard ASAP corpus
8 prompts with >10K essays in total
Prompt-independent AES: 7 prompts are used for training, 1 for testing
Report on common human-machine agreement metrics
Pearsons correlation coefficient (PCC)
Spearmans correlation coefficient (SCC)
Quadratic weighted Kappa (QWK) | We use the standard ASAP corpus
8 prompts with >10K essays in total
Prompt-independent AES: 7 prompts are used for training, 1 for testing
Report on common human-machine agreement metrics
Pearsons correlation coefficient (PCC)
Spearmans correlation coefficient (SCC)
Quadratic weighted Kappa (QWK) | [] |
GEM-SciDuet-train-5#paper-966#slide-10 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-10 | Baselines | RankSVM based on prompt-independent handcrafted
Also used in the prompt-independent stage in TDNN
Two LSTM layer + linear layer
CNN + LSTM + linear layer | RankSVM based on prompt-independent handcrafted
Also used in the prompt-independent stage in TDNN
Two LSTM layer + linear layer
CNN + LSTM + linear layer | [] |
GEM-SciDuet-train-5#paper-966#slide-11 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-11 | RankSVM is the most robust baseline | High variance of DNN models performance on all 8 prompts
Possibly caused by learning on non-target prompts RankSVM appears to be the most stable baseline Justifies the use of RankSVM in the first stage of TDNN | High variance of DNN models performance on all 8 prompts
Possibly caused by learning on non-target prompts RankSVM appears to be the most stable baseline Justifies the use of RankSVM in the first stage of TDNN | [] |
GEM-SciDuet-train-5#paper-966#slide-12 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-12 | Comparison to the best baseline | TDNN outperforms the best baseline on 7 out of 8 prompts Performance improvements gained by learning on the target prompt | TDNN outperforms the best baseline on 7 out of 8 prompts Performance improvements gained by learning on the target prompt | [] |
GEM-SciDuet-train-5#paper-966#slide-13 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Overview",
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-13 | Average performance on 8 prompts | Method QWK PCC SCC | Method QWK PCC SCC | [] |
GEM-SciDuet-train-5#paper-966#slide-14 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-14 | Sanity Check Relative Precision | How the quality of pseudo examples affects the performance of
The sanctity of the selected essays, namely, the number of positive
(negative) essays that are better (worse) than all negative (positive)
Such relative precision is at least 80% and mostly beyond 90% on different prompts
TDNN can at least learn
from correct... | How the quality of pseudo examples affects the performance of
The sanctity of the selected essays, namely, the number of positive
(negative) essays that are better (worse) than all negative (positive)
Such relative precision is at least 80% and mostly beyond 90% on different prompts
TDNN can at least learn
from correct... | [] |
GEM-SciDuet-train-5#paper-966#slide-15 | 966 | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | {
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"Related Work",... | GEM-SciDuet-train-5#paper-966#slide-15 | Conclusions | It is beneficial to learn an AES model on the target prompt
Syntactic features are useful addition to the widely used Word2Vec embeddings
Sanity check: small overlap between pos/neg examples
Prompt-independent AES remains an open problem
TDNN can achieve 0.68 at best | It is beneficial to learn an AES model on the target prompt
Syntactic features are useful addition to the widely used Word2Vec embeddings
Sanity check: small overlap between pos/neg examples
Prompt-independent AES remains an open problem
TDNN can achieve 0.68 at best | [] |
GEM-SciDuet-train-6#paper-970#slide-0 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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... | GEM-SciDuet-train-6#paper-970#slide-0 | The task | Why AstraZeneca plc Dixons Carphone PLC Are Red-Hot Growth
Training data: 1142 samples, 960 headlines/sentences.
Testing data: 491 samples, 461 headlines/sentences. | Why AstraZeneca plc Dixons Carphone PLC Are Red-Hot Growth
Training data: 1142 samples, 960 headlines/sentences.
Testing data: 491 samples, 461 headlines/sentences. | [] |
GEM-SciDuet-train-6#paper-970#slide-1 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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... | GEM-SciDuet-train-6#paper-970#slide-1 | Models | 1. Support Vector Regression (SVR) [1]
2. Bi-directional Long Short-Term Memory BLSTM [2][3] | 1. Support Vector Regression (SVR) [1]
2. Bi-directional Long Short-Term Memory BLSTM [2][3] | [] |
GEM-SciDuet-train-6#paper-970#slide-2 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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... | GEM-SciDuet-train-6#paper-970#slide-2 | Pre Processing and Additional data used | Used 189, 206 financial articles (e.g. Financial Times) that were
manually downloaded from Factiva1 to create a Word2Vec model [5]2.
These were created using Gensim3. | Used 189, 206 financial articles (e.g. Financial Times) that were
manually downloaded from Factiva1 to create a Word2Vec model [5]2.
These were created using Gensim3. | [] |
GEM-SciDuet-train-6#paper-970#slide-3 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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... | GEM-SciDuet-train-6#paper-970#slide-3 | Support Vector Regression SVR 1 | Features and settings that we changed
1. Tokenisation - Whitespace or Unitok4
2. N-grams - uni-grams, bi-grams and both.
3. SVR settings - penalty parameter C and epsilon parameter. | Features and settings that we changed
1. Tokenisation - Whitespace or Unitok4
2. N-grams - uni-grams, bi-grams and both.
3. SVR settings - penalty parameter C and epsilon parameter. | [] |
GEM-SciDuet-train-6#paper-970#slide-4 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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... | GEM-SciDuet-train-6#paper-970#slide-4 | Word Replacements | AstraZeneca PLC had an improved performance where as Dixons
companyname had an posword performance where as companyname | AstraZeneca PLC had an improved performance where as Dixons
companyname had an posword performance where as companyname | [] |
GEM-SciDuet-train-6#paper-970#slide-5 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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... | GEM-SciDuet-train-6#paper-970#slide-5 | Two BLSTM models | Drop out between layers
25 times trained over
Early stopping used to | Drop out between layers
25 times trained over
Early stopping used to | [] |
GEM-SciDuet-train-6#paper-970#slide-7 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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... | GEM-SciDuet-train-6#paper-970#slide-7 | SVR best features | Using uni-grams and bi-grams to be the best. 2.4% improvement
Using a tokeniser always better. Affects bi-gram results the most.
1% improvement using Unitok5 over whitespace.
SVR parameter settings important 8% difference between using
Incorporating the target aspect increased performance. 0.3%
Using all word replaceme... | Using uni-grams and bi-grams to be the best. 2.4% improvement
Using a tokeniser always better. Affects bi-gram results the most.
1% improvement using Unitok5 over whitespace.
SVR parameter settings important 8% difference between using
Incorporating the target aspect increased performance. 0.3%
Using all word replaceme... | [] |
GEM-SciDuet-train-6#paper-970#slide-8 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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... | GEM-SciDuet-train-6#paper-970#slide-8 | Results across the different metrics | Metric 1 was the final metric used. | Metric 1 was the final metric used. | [] |
GEM-SciDuet-train-6#paper-970#slide-9 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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... | GEM-SciDuet-train-6#paper-970#slide-9 | Future Work | 1. Incorporate aspects into the BLSTMs shown to be useful by Wang
2. Improve BLSTMs by using an attention model Wang et al. [7].
3. Add known financial sentiment lexicon into the LSTM model [6]. | 1. Incorporate aspects into the BLSTMs shown to be useful by Wang
2. Improve BLSTMs by using an attention model Wang et al. [7].
3. Add known financial sentiment lexicon into the LSTM model [6]. | [] |
GEM-SciDuet-train-6#paper-970#slide-10 | 970 | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Ter... | {
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0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
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14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
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"1",
"2",
"3",
"4",
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"4.1.1",
"4.1.2",
"4.1.3",
"4.1.4",
"4.1.5",
"4.2",
"3.",
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"4.2.2",
"5",
"6"
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"Related Work",
"Data",
"System description",
... | GEM-SciDuet-train-6#paper-970#slide-10 | Summary | 1. BLSTM outperform SVRs with minimal feature engineering.
2. The future is to incorporate more financial information into the | 1. BLSTM outperform SVRs with minimal feature engineering.
2. The future is to incorporate more financial information into the | [] |
GEM-SciDuet-train-7#paper-971#slide-0 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
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5,
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7,
8,
9,
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35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-0 | Exploring intellectual structures | Collaboration, Author co-citation analysis,
Journal Impact Factor, SJR
Document citation analysis, Co-word analysis,
Citation sentence: Containing brief content of cited work and opinion
that the author of citing work on the cited work
Topic Model: Adopting Author Conference Topic (ACT) model (Tang, Jin
Oncology: The r... | Collaboration, Author co-citation analysis,
Journal Impact Factor, SJR
Document citation analysis, Co-word analysis,
Citation sentence: Containing brief content of cited work and opinion
that the author of citing work on the cited work
Topic Model: Adopting Author Conference Topic (ACT) model (Tang, Jin
Oncology: The r... | [] |
GEM-SciDuet-train-7#paper-971#slide-1 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
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7,
8,
9,
10,
11,
12,
13,
14,
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32,
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34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-1 | Citation Sentence | Embedding useful contents signifying the influence of cited authors on
Being considered as an invisible intellectual place for idea exchanging
Playing a role of supporting and expressing their own arguments by
Exploring the implicit topics resided in citation sentences | Embedding useful contents signifying the influence of cited authors on
Being considered as an invisible intellectual place for idea exchanging
Playing a role of supporting and expressing their own arguments by
Exploring the implicit topics resided in citation sentences | [] |
GEM-SciDuet-train-7#paper-971#slide-2 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
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1,
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3,
4,
5,
6,
7,
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9,
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34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-2 | Original ACT Model Tang Jin and Zhang 2008 | Purpose of Academic search | Purpose of Academic search | [] |
GEM-SciDuet-train-7#paper-971#slide-3 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
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5,
6,
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8,
9,
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32,
33,
34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-3 | Modified AJT Model | 1) Citation Data Extraction
2n d journal Topic 2
Which topic is most salient? Who is the active authors sharing other authors ideas? Which journal leads such endeavor? | 1) Citation Data Extraction
2n d journal Topic 2
Which topic is most salient? Who is the active authors sharing other authors ideas? Which journal leads such endeavor? | [] |
GEM-SciDuet-train-7#paper-971#slide-4 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
"paper_content_id": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
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27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-4 | Method | The 77-SNP PRS was associated with a larg er effect
than previously reported for a 10-SNP-PRS (<xref 3) Citing Authors rid=CIT0020 ref-type=bibr> 20 </xref>). | The 77-SNP PRS was associated with a larg er effect
than previously reported for a 10-SNP-PRS (<xref 3) Citing Authors rid=CIT0020 ref-type=bibr> 20 </xref>). | [] |
GEM-SciDuet-train-7#paper-971#slide-5 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
"paper_content_id": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
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17,
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19,
20,
21,
22,
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27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-5 | Data collection | PubMed Central: 6,360 full-text articles
15 journals of Oncology: by Thomson Reuters JCR & journals impact factor
Cancer Cell, Journal of the National Cancer Institute, Leukemia, Oncogene,
Annals of Oncology, Neuro-Oncology, Stem Cells, Oncotarget, OncoInnunology,
Molecular Oncology, Breast Cancer Research Journal of T... | PubMed Central: 6,360 full-text articles
15 journals of Oncology: by Thomson Reuters JCR & journals impact factor
Cancer Cell, Journal of the National Cancer Institute, Leukemia, Oncogene,
Annals of Oncology, Neuro-Oncology, Stem Cells, Oncotarget, OncoInnunology,
Molecular Oncology, Breast Cancer Research Journal of T... | [] |
GEM-SciDuet-train-7#paper-971#slide-6 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
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1,
2,
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4,
5,
6,
7,
8,
9,
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15,
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27,
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29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-6 | Research Flow | 1) Citation Data Extraction | 1) Citation Data Extraction | [] |
GEM-SciDuet-train-7#paper-971#slide-7 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
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0,
1,
2,
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5,
6,
7,
8,
9,
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11,
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26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-7 | Results 8 Topics | Labeled by 3 Experts
Author Group 1 Author Group 2 Author Group 3 Author Group 4
Journal Group 1 Journal Group 2 Journal Group 3 Journal Group 4
Research Annals of Oncology
Pigment Cell & Melanoma Research
Journal of Thoracic Oncology | Labeled by 3 Experts
Author Group 1 Author Group 2 Author Group 3 Author Group 4
Journal Group 1 Journal Group 2 Journal Group 3 Journal Group 4
Research Annals of Oncology
Pigment Cell & Melanoma Research
Journal of Thoracic Oncology | [] |
GEM-SciDuet-train-7#paper-971#slide-8 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
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0,
1,
2,
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5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
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17,
18,
19,
20,
21,
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23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-8 | Results contd | Author Group 5 Author Group 6 Author Group Author Group 8
Journal Group 5 Journal Group 6 Journal Group 7 Journal Group 8
Annals of Oncology Cancer Cell
Annals of Oncology Breast Cancer Research | Author Group 5 Author Group 6 Author Group Author Group 8
Journal Group 5 Journal Group 6 Journal Group 7 Journal Group 8
Annals of Oncology Cancer Cell
Annals of Oncology Breast Cancer Research | [] |
GEM-SciDuet-train-7#paper-971#slide-9 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
"paper_content_id": [
0,
1,
2,
3,
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5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-9 | Conclusion | AJT model: to detect leading authors and journals in sub-disciplines
represented by discovered topics in a certain field
Citation sentences: Discovering latent meaning associated citation sentences
and the major players leading the field | AJT model: to detect leading authors and journals in sub-disciplines
represented by discovered topics in a certain field
Citation sentences: Discovering latent meaning associated citation sentences
and the major players leading the field | [] |
GEM-SciDuet-train-7#paper-971#slide-10 | 971 | Exploring the leading authors and journals in major topics by citation sentences and topic modeling | Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the to... | {
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23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2.1",
"2.2",
"2.4",
"4"
],
"paper_header_content": [
"Introduction",
"Main idea",
"Data collection",
"AJT Model",
"Conclusion"
]
} | GEM-SciDuet-train-7#paper-971#slide-10 | Future works | Comparing the proposed approach with the general topic modeling
Investigating whether there is a different impact of using citation
sentences and general meta-data (abstract and title)
Considering the window size of citation sentences enriching citation | Comparing the proposed approach with the general topic modeling
Investigating whether there is a different impact of using citation
sentences and general meta-data (abstract and title)
Considering the window size of citation sentences enriching citation | [] |
GEM-SciDuet-train-8#paper-972#slide-0 | 972 | Obtaining SMT dictionaries for related languages | This study explores methods for developing Machine Translation dictionaries on the basis of word frequency lists coming from comparable corpora. We investigate (1) various methods to measure the similarity of cognates between related languages, (2) detection and removal of noisy cognate translations using SVM ranking. ... | {
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0,
1,
2,
3,
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5,
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7,
8,
9,
10,
11,
12,
13,
14,
15,
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18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
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"1",
"2",
"2.1",
"2.2",
"3",
"3.1",
"3.2",
"3.3",
"4"
],
"paper_header_content": [
"Introduction",
"Methodology",
"Cognate detection",
"Cognate Ranking",
"Results and Discussion",
"Data",
"Evaluation of the Ranking Model",
... | GEM-SciDuet-train-8#paper-972#slide-0 | Motivation | Extracting cognates for related languages in Romance and
Reducing the number of unknown words on SMT training data
Learning regular differences in words roots/endings shared across related languages | Extracting cognates for related languages in Romance and
Reducing the number of unknown words on SMT training data
Learning regular differences in words roots/endings shared across related languages | [] |
GEM-SciDuet-train-8#paper-972#slide-1 | 972 | Obtaining SMT dictionaries for related languages | This study explores methods for developing Machine Translation dictionaries on the basis of word frequency lists coming from comparable corpora. We investigate (1) various methods to measure the similarity of cognates between related languages, (2) detection and removal of noisy cognate translations using SVM ranking. ... | {
"paper_content_id": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
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24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
"paper_header_number": [
"1",
"2",
"2.1",
"2.2",
"3",
"3.1",
"3.2",
"3.3",
"4"
],
"paper_header_content": [
"Introduction",
"Methodology",
"Cognate detection",
"Cognate Ranking",
"Results and Discussion",
"Data",
"Evaluation of the Ranking Model",
... | GEM-SciDuet-train-8#paper-972#slide-1 | Method | Produce n-best lists of cognates using a family of distance measures from comparable corpora
Prune the n-best lists by ranking Machine Learning (ML) algorithm trained over parallel corpora
Motivation n-best list allows surface variation on possible cognate translations | Produce n-best lists of cognates using a family of distance measures from comparable corpora
Prune the n-best lists by ranking Machine Learning (ML) algorithm trained over parallel corpora
Motivation n-best list allows surface variation on possible cognate translations | [] |
GEM-SciDuet-train-8#paper-972#slide-2 | 972 | Obtaining SMT dictionaries for related languages | This study explores methods for developing Machine Translation dictionaries on the basis of word frequency lists coming from comparable corpora. We investigate (1) various methods to measure the similarity of cognates between related languages, (2) detection and removal of noisy cognate translations using SVM ranking. ... | {
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0,
1,
2,
3,
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7,
8,
9,
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11,
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14,
15,
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17,
18,
19,
20,
21,
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24,
25,
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27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
... | {
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"2.1",
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"3.1",
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"Introduction",
"Methodology",
"Cognate detection",
"Cognate Ranking",
"Results and Discussion",
"Data",
"Evaluation of the Ranking Model",
... | GEM-SciDuet-train-8#paper-972#slide-2 | Similarity metrics | Compare words between frequency lists over comparable corpora
L matching between the languages using Levenshtein distance:
L-R Levenshtein distance computed separately for the roots and for the endings: aceito (pt) vs acepto (es) rejeito (pt) vs rechazo (es)
L-C Levenshtein distance over words with similar number of st... | Compare words between frequency lists over comparable corpora
L matching between the languages using Levenshtein distance:
L-R Levenshtein distance computed separately for the roots and for the endings: aceito (pt) vs acepto (es) rejeito (pt) vs rechazo (es)
L-C Levenshtein distance over words with similar number of st... | [] |
GEM-SciDuet-train-8#paper-972#slide-3 | 972 | Obtaining SMT dictionaries for related languages | This study explores methods for developing Machine Translation dictionaries on the basis of word frequency lists coming from comparable corpora. We investigate (1) various methods to measure the similarity of cognates between related languages, (2) detection and removal of noisy cognate translations using SVM ranking. ... | {
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... | GEM-SciDuet-train-8#paper-972#slide-3 | Search space constraints | Motivation Exhaustive method compares all the combinations of source and target words
Order the target side frequency list into bins of similar frequency
Compare each source word with target bins of similar frequency around a window
L-C metric only compares words that share a given n prefix | Motivation Exhaustive method compares all the combinations of source and target words
Order the target side frequency list into bins of similar frequency
Compare each source word with target bins of similar frequency around a window
L-C metric only compares words that share a given n prefix | [] |
GEM-SciDuet-train-8#paper-972#slide-4 | 972 | Obtaining SMT dictionaries for related languages | This study explores methods for developing Machine Translation dictionaries on the basis of word frequency lists coming from comparable corpora. We investigate (1) various methods to measure the similarity of cognates between related languages, (2) detection and removal of noisy cognate translations using SVM ranking. ... | {
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... | GEM-SciDuet-train-8#paper-972#slide-4 | Ranking | Motivation Prune n-best lists by ranking ML algorithm
Training data come from aligned parallel corpora where the rank is given by the alignment probability from GIZA++
Simulate cognate training data by pruning pairs of words below a Levenshtein threshold | Motivation Prune n-best lists by ranking ML algorithm
Training data come from aligned parallel corpora where the rank is given by the alignment probability from GIZA++
Simulate cognate training data by pruning pairs of words below a Levenshtein threshold | [] |
GEM-SciDuet-train-8#paper-972#slide-5 | 972 | Obtaining SMT dictionaries for related languages | This study explores methods for developing Machine Translation dictionaries on the basis of word frequency lists coming from comparable corpora. We investigate (1) various methods to measure the similarity of cognates between related languages, (2) detection and removal of noisy cognate translations using SVM ranking. ... | {
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... | GEM-SciDuet-train-8#paper-972#slide-5 | Features | Number of times of each edit operation, the model assigns a different weight to each operation
Cosine between the distributional vectors of the source and target words vectors from word2vec mapped to same space via a learned transformation matrix
SVM ranking default configuration (RBF kernel)
Easy-adapt features given ... | Number of times of each edit operation, the model assigns a different weight to each operation
Cosine between the distributional vectors of the source and target words vectors from word2vec mapped to same space via a learned transformation matrix
SVM ranking default configuration (RBF kernel)
Easy-adapt features given ... | [] |
GEM-SciDuet-train-8#paper-972#slide-6 | 972 | Obtaining SMT dictionaries for related languages | This study explores methods for developing Machine Translation dictionaries on the basis of word frequency lists coming from comparable corpora. We investigate (1) various methods to measure the similarity of cognates between related languages, (2) detection and removal of noisy cognate translations using SVM ranking. ... | {
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... | GEM-SciDuet-train-8#paper-972#slide-6 | Data description | n-best lists from Wikipedia dumps (frequency lists)
ML training Wiki-titles, parallel data from inter language links from the tittles of the Wikipedia articles 500K aligned links (i.e. sentences)
Opensubs, 90K training instances
Zoo proprietary corpus of subtitles produced by professional translators, 20K training inst... | n-best lists from Wikipedia dumps (frequency lists)
ML training Wiki-titles, parallel data from inter language links from the tittles of the Wikipedia articles 500K aligned links (i.e. sentences)
Opensubs, 90K training instances
Zoo proprietary corpus of subtitles produced by professional translators, 20K training inst... | [] |
GEM-SciDuet-train-8#paper-972#slide-7 | 972 | Obtaining SMT dictionaries for related languages | This study explores methods for developing Machine Translation dictionaries on the basis of word frequency lists coming from comparable corpora. We investigate (1) various methods to measure the similarity of cognates between related languages, (2) detection and removal of noisy cognate translations using SVM ranking. ... | {
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... | GEM-SciDuet-train-8#paper-972#slide-7 | Language pairs | Romance Source: Portuguese, French, Italian Target: Spanish
Slavonic Source: Ukrainian, Bulgarian Target: Russian | Romance Source: Portuguese, French, Italian Target: Spanish
Slavonic Source: Ukrainian, Bulgarian Target: Russian | [] |
GEM-SciDuet-train-8#paper-972#slide-8 | 972 | Obtaining SMT dictionaries for related languages | This study explores methods for developing Machine Translation dictionaries on the basis of word frequency lists coming from comparable corpora. We investigate (1) various methods to measure the similarity of cognates between related languages, (2) detection and removal of noisy cognate translations using SVM ranking. ... | {
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... | GEM-SciDuet-train-8#paper-972#slide-8 | Results on heldout data | Error score on heldout data
E Edit distance features
EC Edit distance plus distributed vectors features
Zoo error% Opensubs error% Wiki-titles error%
Romance pt-es it-es fr-es
Model E Model EC Model E Model EC Model E Model EC | Error score on heldout data
E Edit distance features
EC Edit distance plus distributed vectors features
Zoo error% Opensubs error% Wiki-titles error%
Romance pt-es it-es fr-es
Model E Model EC Model E Model EC Model E Model EC | [] |
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