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GEM-SciDuet-train-16#paper-994#slide-2 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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GEM-SciDuet-train-16#paper-994#slide-3 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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"Additio... | GEM-SciDuet-train-16#paper-994#slide-3 | Conservatism in MT Based Simplification | In both SMT and NMT Text Simplification, a large proportion of the input sentences are not modified. (Alva-Manchego et al., 2017; on the Newsela corpus).
It is confirmed in the present work (experiments on Wikipedia):
of the input sentences remain unchanged.
- None of the references are identical to the source.
- Accor... | In both SMT and NMT Text Simplification, a large proportion of the input sentences are not modified. (Alva-Manchego et al., 2017; on the Newsela corpus).
It is confirmed in the present work (experiments on Wikipedia):
of the input sentences remain unchanged.
- None of the references are identical to the source.
- Accor... | [] |
GEM-SciDuet-train-16#paper-994#slide-4 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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"Additio... | GEM-SciDuet-train-16#paper-994#slide-4 | Sentence Splitting in Text Simplification | Splitting in NMT-Based Simplification
Sentence splitting is not addressed.
Rareness of splittings in the simplification training corpora.
Recently, corpus focusing on sentence splitting for the Split-and-Rephrase task
(Narayan et al., 2017) where the other operations are not addressed.
Directly modeling sentence splitt... | Splitting in NMT-Based Simplification
Sentence splitting is not addressed.
Rareness of splittings in the simplification training corpora.
Recently, corpus focusing on sentence splitting for the Split-and-Rephrase task
(Narayan et al., 2017) where the other operations are not addressed.
Directly modeling sentence splitt... | [] |
GEM-SciDuet-train-16#paper-994#slide-5 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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"Additio... | GEM-SciDuet-train-16#paper-994#slide-5 | Direct Semantic Splitting DSS | A simple algorithm that directly decomposes the sentence into its semantic components, using 2 splitting rules.
The splitting is directed by semantic parsing.
The semantic annotation directly captures shared arguments.
It can be used as a preprocessing step for other simplification operations.
Input sentence Split sent... | A simple algorithm that directly decomposes the sentence into its semantic components, using 2 splitting rules.
The splitting is directed by semantic parsing.
The semantic annotation directly captures shared arguments.
It can be used as a preprocessing step for other simplification operations.
Input sentence Split sent... | [] |
GEM-SciDuet-train-16#paper-994#slide-6 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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- Based on typological and cognitive theories
L A A and P
He came back home played piano
Parallel Scene (H) Linker (L) P surprised His Participant (A) Process (P) arrival
E observed E C A A Parallel Scene (H) the planet R S C Participant (A) Process (P) State (S) E ... | Semantic Annotation: UCCA (Abend and Rappoport, 2013)
- Based on typological and cognitive theories
L A A and P
He came back home played piano
Parallel Scene (H) Linker (L) P surprised His Participant (A) Process (P) arrival
E observed E C A A Parallel Scene (H) the planet R S C Participant (A) Process (P) State (S) E ... | [] |
GEM-SciDuet-train-16#paper-994#slide-7 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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Fits with event-wise simplification (Glavas and Stajner, 2013)
Here we only use semantic criteria.
It was also investigated in the context of Text Simplification evaluation:
SAMSA measure (Sulem, Abend and Rappoport, NAACL 2018)
A A and He came back home and played piano.
He ... | Placing each Scene in a different sentence.
Fits with event-wise simplification (Glavas and Stajner, 2013)
Here we only use semantic criteria.
It was also investigated in the context of Text Simplification evaluation:
SAMSA measure (Sulem, Abend and Rappoport, NAACL 2018)
A A and He came back home and played piano.
He ... | [] |
GEM-SciDuet-train-16#paper-994#slide-8 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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We use a state-of-the-art NMT-Based TS system, NTS (Nisioi et al., 2017).
The combined system is called SENTS.
NTS was built using the OpenNMT (Klein et al., 2017) framework.
We use the NTS-w2v provided model where word2vec embeddings are used for the i... | After DSS, the output is fed to an MT-based simplification system.
We use a state-of-the-art NMT-Based TS system, NTS (Nisioi et al., 2017).
The combined system is called SENTS.
NTS was built using the OpenNMT (Klein et al., 2017) framework.
We use the NTS-w2v provided model where word2vec embeddings are used for the i... | [] |
GEM-SciDuet-train-16#paper-994#slide-9 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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"Additio... | GEM-SciDuet-train-16#paper-994#slide-9 | Experiments | Test set of Xu et al., 2016: sentences, each with 8 references
e.g., percentage of sentences copied from the input (%Same)
First 70 sentences of the corpus
3 annotators native English speakers
4 questions for each input-output pair
Is the output fluent and grammatical?
Does the output preserve the meaning of the input?... | Test set of Xu et al., 2016: sentences, each with 8 references
e.g., percentage of sentences copied from the input (%Same)
First 70 sentences of the corpus
3 annotators native English speakers
4 questions for each input-output pair
Is the output fluent and grammatical?
Does the output preserve the meaning of the input?... | [] |
GEM-SciDuet-train-16#paper-994#slide-10 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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"Additio... | GEM-SciDuet-train-16#paper-994#slide-10 | Results | BLEU SARI G M S StS
Automatic evaluation: BLEU, SARI
Human evaluation (first 70 sentences):
G Grammaticality: 1 to 5 scale S Simplicity: -2 to +2 scale
P Meaning Preservation: 1 to 5 scale StS Structural Simplicity: -2 to +2 scale
Identity gets the highest BLEU score and the lowest SARI scores.
The two SENTS systems ou... | BLEU SARI G M S StS
Automatic evaluation: BLEU, SARI
Human evaluation (first 70 sentences):
G Grammaticality: 1 to 5 scale S Simplicity: -2 to +2 scale
P Meaning Preservation: 1 to 5 scale StS Structural Simplicity: -2 to +2 scale
Identity gets the highest BLEU score and the lowest SARI scores.
The two SENTS systems ou... | [] |
GEM-SciDuet-train-16#paper-994#slide-12 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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"Additio... | GEM-SciDuet-train-16#paper-994#slide-12 | Conclusion 1 | We presented here the first simplification system combining semantic structures and neural machine translation.
Our system compares favorably to the state-of-the-art in combined structural and lexical simplification.
This approach addresses the conservatism of MT-based systems.
Sentence splitting is performed without r... | We presented here the first simplification system combining semantic structures and neural machine translation.
Our system compares favorably to the state-of-the-art in combined structural and lexical simplification.
This approach addresses the conservatism of MT-based systems.
Sentence splitting is performed without r... | [] |
GEM-SciDuet-train-16#paper-994#slide-13 | 994 | Simple and Effective Text Simplification Using Semantic and Neural Methods | Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used ... | {
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Sulem, Abend and Rappoport, NAACL 2018)
Future work will leverage UCCAs cross-linguistic applicability to support multi-lingual text simplification and simplification pre-processing for MT. | Sentence splitting is treated as the decomposition of the sentence into its Scenes (as in SAMSA evaluation measure;
Sulem, Abend and Rappoport, NAACL 2018)
Future work will leverage UCCAs cross-linguistic applicability to support multi-lingual text simplification and simplification pre-processing for MT. | [] |
GEM-SciDuet-train-17#paper-1001#slide-1 | 1001 | Consistent Improvement in Translation Quality of Chinese-Japanese Technical Texts by Adding Additional Quasi-parallel Training Data | Bilingual parallel corpora are an extremely important resource as they are typically used in data-driven machine translation. There already exist many freely available corpora for European languages, but almost none between Chinese and Japanese. The constitution of large bilingual corpora is a problem for less document... | {
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"Chinese and Japanese M... | GEM-SciDuet-train-17#paper-1001#slide-1 | SMT Experiments | Experimental results of SMT
BLEU NIST WER TER RIBES baseline zh-ja + additional training data
Table: Evaluation results for ChineseJapanese translation across two
SMT systems (baseline and baseline + additional quasi-parallel data),
Moses version: 1.0, segmentation tools: urheen and mecab. | Experimental results of SMT
BLEU NIST WER TER RIBES baseline zh-ja + additional training data
Table: Evaluation results for ChineseJapanese translation across two
SMT systems (baseline and baseline + additional quasi-parallel data),
Moses version: 1.0, segmentation tools: urheen and mecab. | [] |
GEM-SciDuet-train-18#paper-1009#slide-0 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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"Problem Definiti... | GEM-SciDuet-train-18#paper-1009#slide-0 | Data is Limited | Most of the popular models in NLP are data-driven
We often need to operate in a specific scenario Limited data
Take spoken language understanding as an example
Need to be implemented for many domains Limited data
Intent Detection flights from Boston to Tokyo intent: flight
Slot Filling flights from Boston to Tokyo from... | Most of the popular models in NLP are data-driven
We often need to operate in a specific scenario Limited data
Take spoken language understanding as an example
Need to be implemented for many domains Limited data
Intent Detection flights from Boston to Tokyo intent: flight
Slot Filling flights from Boston to Tokyo from... | [] |
GEM-SciDuet-train-18#paper-1009#slide-1 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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Regular expression is the most commonly used rule in NLP
Many regular expression rules in company
Intent Detection flights from Boston to Tokyo intent: flight
Slot Filling flights from Boston to Tokyo fromloc.city:Bostontoloc.city:Tokyo
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Regular expression is the most commonly used rule in NLP
Many regular expression rules in company
Intent Detection flights from Boston to Tokyo intent: flight
Slot Filling flights from Boston to Tokyo fromloc.city:Bostontoloc.city:Tokyo
However, regular expressions are hard to... | [] |
GEM-SciDuet-train-18#paper-1009#slide-2 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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flights? from/ flights from Boston to Tokyo intent: flight
flights from Boston to Tokyo fromloc.city:Bostontoloc.city:Tokyo
RE contains clue words
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flights? from/ flights from Boston to Tokyo intent: flight
flights from Boston to Tokyo fromloc.city:Bostontoloc.city:Tokyo
RE contains clue words
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GEM-SciDuet-train-18#paper-1009#slide-3 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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REtag: flight Softmax Classifier
RE feat s Attention
Intent Detection h1 h2 h3 h4 h5
BLSTM RE Instance x1 x2 x3 x4 x5
flights from Boston to Miami /^flights? from/
flights from Boston to Miami REtag: O O B-loc.city O B-loc.city | Embed the REtag, append to input
REtag: flight Softmax Classifier
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flights from Boston to Miami /^flights? from/
flights from Boston to Miami REtag: O O B-loc.city O B-loc.city | [] |
GEM-SciDuet-train-18#paper-1009#slide-4 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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, whether regular expression predict class k
Intent: flight logitk=l ogitk+ w kzk Softmax Classifier
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flights from Boston to Miami /^flights? from/
Slot Filling h1 h2 h3 h4 h5
flights from Boston to /from... | [] |
GEM-SciDuet-train-18#paper-1009#slide-5 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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Positive Regular Expressions (REs) Negative REs
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flights from Boston to Miami Gold Att:
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GEM-SciDuet-train-18#paper-1009#slide-6 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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We want to answer the following questions:
Can regular expressions (REs) improve the neural network (NN) when
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We want to answer the following questions:
Can regular expressions (REs) improve the neural network (NN) when
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Can REs still improve NN when using the full dataset?
How does RE complexity influence the results? | [] |
GEM-SciDuet-train-18#paper-1009#slide-7 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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Using RE output as feature performs best for slot filling | Using clue words to guide attention performs best for intent detection
Using RE output as feature performs best for slot filling | [] |
GEM-SciDuet-train-18#paper-1009#slide-8 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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GEM-SciDuet-train-18#paper-1009#slide-9 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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Complex RE: /(_AIRCRAFT_CODE) that fly/
Complex Simple Complex Simple
Complex REs yield better results
Simple REs also clearly improves the baseline | Complex RE: many semantically independant groups
Complex RE: /(_AIRCRAFT_CODE) that fly/
Complex Simple Complex Simple
Complex REs yield better results
Simple REs also clearly improves the baseline | [] |
GEM-SciDuet-train-18#paper-1009#slide-10 | 1009 | Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding | The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answ... | {
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Guiding attention is best for intent detection (sentence classification)
RE output as feature is best for slot filling (sequence labeling)
We can start with simple REs, and increase complexity gradually | Using REs can help to train of NN when data is limited
Guiding attention is best for intent detection (sentence classification)
RE output as feature is best for slot filling (sequence labeling)
We can start with simple REs, and increase complexity gradually | [] |
GEM-SciDuet-train-19#paper-1013#slide-0 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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Example: searching vegetarian in biblical scholarship archive
Were All Men Vegetarians
God instructed Adam saying,
I have given you
every herb that yields
Of every tree of the garden
thou mayest freely eat:
and thou shalt eat the
herb of the field;
(King James Bible, Genesis)
(by Eric ... | Domain Specific Diachronic Corpus
Example: searching vegetarian in biblical scholarship archive
Were All Men Vegetarians
God instructed Adam saying,
I have given you
every herb that yields
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(King James Bible, Genesis)
(by Eric ... | [] |
GEM-SciDuet-train-19#paper-1013#slide-1 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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Target term vegetarian modern
Related terms tree of the garden herb of the field ancient
Users are mostly aware of modern language
Collecting relevant related terms
For given thesaurus entries
Collecting a relevant list of modern target terms | A useful tool for supporting searches in diachronic corpus
Target term vegetarian modern
Related terms tree of the garden herb of the field ancient
Users are mostly aware of modern language
Collecting relevant related terms
For given thesaurus entries
Collecting a relevant list of modern target terms | [] |
GEM-SciDuet-train-19#paper-1013#slide-2 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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Predict which candidates are relevant for the domain corpus | Utilize a given candidate list of modern terms as input
Predict which candidates are relevant for the domain corpus | [] |
GEM-SciDuet-train-19#paper-1013#slide-3 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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"Re... | GEM-SciDuet-train-19#paper-1013#slide-3 | Background Terminology Extraction TE | 1. Automatically extract prominent terms from a given corpus
SSccoorree ccaannddiiddaattee tteerrmmss ffoorr ddoommaaiinn rreelleevvaannccyy
Statistical measures for identifying prominent terms
Frequencies in the target corpus (e.g. tf, tf-idf)
Comparison with frequencies in a reference background corpus | 1. Automatically extract prominent terms from a given corpus
SSccoorree ccaannddiiddaattee tteerrmmss ffoorr ddoommaaiinn rreelleevvaannccyy
Statistical measures for identifying prominent terms
Frequencies in the target corpus (e.g. tf, tf-idf)
Comparison with frequencies in a reference background corpus | [] |
GEM-SciDuet-train-19#paper-1013#slide-4 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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"Re... | GEM-SciDuet-train-19#paper-1013#slide-4 | Supervised framework for TE | Candidate target terms are learning instances
Calculate a set of features for each candidate
Classification predicts which candidates are suitable
Features : state-of-the-art TE scoring measures | Candidate target terms are learning instances
Calculate a set of features for each candidate
Classification predicts which candidates are suitable
Features : state-of-the-art TE scoring measures | [] |
GEM-SciDuet-train-19#paper-1013#slide-5 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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"Re... | GEM-SciDuet-train-19#paper-1013#slide-5 | Contributions | Integrating Query Performance Prediction in term scoring
2. Penetrating to ancient texts, via query expansion | Integrating Query Performance Prediction in term scoring
2. Penetrating to ancient texts, via query expansion | [] |
GEM-SciDuet-train-19#paper-1013#slide-6 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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"Re... | GEM-SciDuet-train-19#paper-1013#slide-6 | Contribution 1 | Integrating Query Performance Prediction
Penetrating to ancient texts | Integrating Query Performance Prediction
Penetrating to ancient texts | [] |
GEM-SciDuet-train-19#paper-1013#slide-7 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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"Re... | GEM-SciDuet-train-19#paper-1013#slide-7 | Query Performance Prediction QPP | Estimate the retrieval quality of search queries
Assess quality of query results on the text collection.
Our terminology scoring task
QPP scoring measures are potentially useful may capture
additional aspects of term relevancy for the collection
term is relevant for a domain term is a good query
Two types of statistica... | Estimate the retrieval quality of search queries
Assess quality of query results on the text collection.
Our terminology scoring task
QPP scoring measures are potentially useful may capture
additional aspects of term relevancy for the collection
term is relevant for a domain term is a good query
Two types of statistica... | [] |
GEM-SciDuet-train-19#paper-1013#slide-8 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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"Re... | GEM-SciDuet-train-19#paper-1013#slide-8 | Penetrating to ancient periods | In a diachronic corpus
A candidate term might be rare in its original modern form,
yet frequently referred to by archaic forms
query term: vegetarian Of every tree of the garden
thou mayest freely eat: every herb that yields
Were All Men Vegetarians
God instructed Adam saying, I have given you every herb that yields (G... | In a diachronic corpus
A candidate term might be rare in its original modern form,
yet frequently referred to by archaic forms
query term: vegetarian Of every tree of the garden
thou mayest freely eat: every herb that yields
Were All Men Vegetarians
God instructed Adam saying, I have given you every herb that yields (G... | [] |
GEM-SciDuet-train-19#paper-1013#slide-9 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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"Re... | GEM-SciDuet-train-19#paper-1013#slide-9 | Evaluation Setting | Diachronic corpus: the Responsa Project
Questions posed to rabbis along their detailed rabbinic answers
Written over a period of about a thousand years
Used for previous IR and NLP research
Balanced for positive and negative examples
Support Vector Machine with polynomial kernel | Diachronic corpus: the Responsa Project
Questions posed to rabbis along their detailed rabbinic answers
Written over a period of about a thousand years
Used for previous IR and NLP research
Balanced for positive and negative examples
Support Vector Machine with polynomial kernel | [] |
GEM-SciDuet-train-19#paper-1013#slide-10 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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"Re... | GEM-SciDuet-train-19#paper-1013#slide-10 | Results | Additional QPP features increase the classification accuracy
Utilizing ancient documents, via query expansion, improves
Improvement over baseline statistically significant | Additional QPP features increase the classification accuracy
Utilizing ancient documents, via query expansion, improves
Improvement over baseline statistically significant | [] |
GEM-SciDuet-train-19#paper-1013#slide-11 | 1013 | Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus | A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, whi... | {
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"Re... | GEM-SciDuet-train-19#paper-1013#slide-11 | Summary | Task: target term selection for a diachronic thesaurus
Integrating Query Performance Prediction in Term Scoring
2. Penetrating to ancient texts via query expansion
Utilize additional query expansion algorithms
Investigate the selective query expansion approach | Task: target term selection for a diachronic thesaurus
Integrating Query Performance Prediction in Term Scoring
2. Penetrating to ancient texts via query expansion
Utilize additional query expansion algorithms
Investigate the selective query expansion approach | [] |
GEM-SciDuet-train-20#paper-1018#slide-0 | 1018 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | {
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Reasoning: The process of forming conclusions, judgments, or inferences from facts or premises.
Inferential Reasoning: Premise 1, Premise 2 -> Conclusion
John is in the kitchen, John has the ball -> The ball is in the kitchen
R... | Reasoning is crucial for building systems that can dialogue with humans in natural language.
Reasoning: The process of forming conclusions, judgments, or inferences from facts or premises.
Inferential Reasoning: Premise 1, Premise 2 -> Conclusion
John is in the kitchen, John has the ball -> The ball is in the kitchen
R... | [] |
GEM-SciDuet-train-20#paper-1018#slide-1 | 1018 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | {
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"Memory N... | GEM-SciDuet-train-20#paper-1018#slide-1 | bAbI Dataset | One of the earliest datasets to measure Category 2: Two Supporting Facts. the reasoning abilities of ML systems.
Mary went to the kitchen.
Sandra journeyed to the office
Mary got the football there. Is(Football, Garden)
Mary travelled to the garden.
Where is the football? garden
Easy to evaluate different reasoning cap... | One of the earliest datasets to measure Category 2: Two Supporting Facts. the reasoning abilities of ML systems.
Mary went to the kitchen.
Sandra journeyed to the office
Mary got the football there. Is(Football, Garden)
Mary travelled to the garden.
Where is the football? garden
Easy to evaluate different reasoning cap... | [] |
GEM-SciDuet-train-20#paper-1018#slide-2 | 1018 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | {
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01: Daniel went to the bathroom. 02: Sandra journeyed to the office. 03: Mary got the football there... | Process a set of inputs and store them in memory. Then, at each hop, an important part of the memory is retrieved and used to retrieve more memories. Finally, the last retrieved memory is used i to compute the answer. y
01: Daniel went to the bathroom. 02: Sandra journeyed to the office. 03: Mary got the football there... | [] |
GEM-SciDuet-train-20#paper-1018#slide-3 | 1018 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | {
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Neural Network with an inductive bias to learn pairwise relations of the input objects and their properties. A type of Graph Neural Networks. L(y, y) yiln( yi)
01: Daniel went to the bathroom.
02: Sandra journeyed to the office.
03: Mary got the football there.
04: Mary travelled... | Relation Networks (Santoro et al. 2017)
Neural Network with an inductive bias to learn pairwise relations of the input objects and their properties. A type of Graph Neural Networks. L(y, y) yiln( yi)
01: Daniel went to the bathroom.
02: Sandra journeyed to the office.
03: Mary got the football there.
04: Mary travelled... | [] |
GEM-SciDuet-train-20#paper-1018#slide-4 | 1018 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | {
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Short-term Memory Module Attention Module Reasoning Module
01: Daniel went to the bathroom. 02: Sandra journeyed to the ... | A Memory Network model with a new working memory buffer and relational reasoning module. Produces state-of-the-art results in reasoning tasks. Inspired by the Multi-component model of working memory.
Short-term Memory Module Attention Module Reasoning Module
01: Daniel went to the bathroom. 02: Sandra journeyed to the ... | [] |
GEM-SciDuet-train-20#paper-1018#slide-5 | 1018 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | {
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"Memory N... | GEM-SciDuet-train-20#paper-1018#slide-5 | Results Jointly trained bAbI 10k | Note that EntNet (Henaff et al.) solves all tasks in the per-task version: A single model for each task.
LSTM MemNN MemNN-S (Sukhbaatar et al.) (Sukhbaatar et al.) (Sukhbaatar et al.) RN (Santoro et al.) SDNC (Rae et al.) WMemNN (Pavez et al.) WMemNN* (Pavez et al.) | Note that EntNet (Henaff et al.) solves all tasks in the per-task version: A single model for each task.
LSTM MemNN MemNN-S (Sukhbaatar et al.) (Sukhbaatar et al.) (Sukhbaatar et al.) RN (Santoro et al.) SDNC (Rae et al.) WMemNN (Pavez et al.) WMemNN* (Pavez et al.) | [] |
GEM-SciDuet-train-20#paper-1018#slide-6 | 1018 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | {
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2 supporting facts 3 supporting facts counting basic induction size reasoning positional reasoning path finding | complex attention patterns multiple relations
2 supporting facts 3 supporting facts counting basic induction size reasoning positional reasoning path finding | [] |
GEM-SciDuet-train-20#paper-1018#slide-7 | 1018 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | {
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GEM-SciDuet-train-20#paper-1018#slide-8 | 1018 | Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module | During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that c... | {
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It retains the relational reasoning capabilities of the relation network while reducing it computation times considerably.
We hope that this contribution may help applying t... | We presented the Working Memory Neural Network, a Memory Network model augmented with a new working memory buffer and relational reasoning module.
It retains the relational reasoning capabilities of the relation network while reducing it computation times considerably.
We hope that this contribution may help applying t... | [] |
GEM-SciDuet-train-21#paper-1019#slide-0 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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Iterative training: Online search with initialization MML over offline search output
Coverage during online search
State-of-the-art single model performances:
WikiTab... | Training semantic parsing with denotation-only supervision is challenging because of spuriousness: incorrect logical forms can yield correct denotations.
Iterative training: Online search with initialization MML over offline search output
Coverage during online search
State-of-the-art single model performances:
WikiTab... | [] |
GEM-SciDuet-train-21#paper-1019#slide-1 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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"Weakly supervised semant... | GEM-SciDuet-train-21#paper-1019#slide-1 | Semantic Parsing for Question Answering | Question: Which athlete was from South Korea
Get rows where Nation is South Korea
Filter rows where value in Olympics
Get value from Athlete column
Kim Yu-na South Korea (KOR)
south_korea) athlete) Patrick Chan Canada (CAN)
WikiTableQuestions, Pasupat and Liang (2015) | Question: Which athlete was from South Korea
Get rows where Nation is South Korea
Filter rows where value in Olympics
Get value from Athlete column
Kim Yu-na South Korea (KOR)
south_korea) athlete) Patrick Chan Canada (CAN)
WikiTableQuestions, Pasupat and Liang (2015) | [] |
GEM-SciDuet-train-21#paper-1019#slide-2 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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wi: Kim Yu-na South Korea
Tenley Albright United States
Test: Given find such that | xi: Which athlete was from South Korea after 2010?
wi: Kim Yu-na South Korea
Tenley Albright United States
Test: Given find such that | [] |
GEM-SciDuet-train-21#paper-1019#slide-3 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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Logical forms that lead to answer:
(reverse athlete)(and(nation s outh_korea)(year ((reverse date) Athlete from South Korea after 2010
Plushenko Russia (RUS) (reverse athlete)(and(nation Athlete from South Korea with 2 medals s outh_korea)(medals 2)))
Kim Yu-na South Korea (KOR... | Which athletes are from South Korea after
Logical forms that lead to answer:
(reverse athlete)(and(nation s outh_korea)(year ((reverse date) Athlete from South Korea after 2010
Plushenko Russia (RUS) (reverse athlete)(and(nation Athlete from South Korea with 2 medals s outh_korea)(medals 2)))
Kim Yu-na South Korea (KOR... | [] |
GEM-SciDuet-train-21#paper-1019#slide-4 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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Krishn amurthy et al. (2017), and others Proposal: Alternate between the two objectives while gradually and others
increasing the s earch space!
Maxim ize the marginal likelihood of an approximate set of logical forms
Minimum Bayes Risk training: Minimize the ex... | Maximum Marginal Likelihood Reward/Cost -based approaches
Krishn amurthy et al. (2017), and others Proposal: Alternate between the two objectives while gradually and others
increasing the s earch space!
Maxim ize the marginal likelihood of an approximate set of logical forms
Minimum Bayes Risk training: Minimize the ex... | [] |
GEM-SciDuet-train-21#paper-1019#slide-5 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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Max logical form depth = k + s
Step 1: Train model using MML on seed set
Step 2: Train using MBR on all data till a greater depth k + s
Minimum Bayes Risk training till depth k + s
Step 3: Replace offline search with trained MBR and upda... | Limited depth exhaustive search
Step 0: Get seed set of logical forms till depth k
Max logical form depth = k + s
Step 1: Train model using MML on seed set
Step 2: Train using MBR on all data till a greater depth k + s
Minimum Bayes Risk training till depth k + s
Step 3: Replace offline search with trained MBR and upda... | [] |
GEM-SciDuet-train-21#paper-1019#slide-6 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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(count_equals (square (touch_bottom all_objects))
Insight: There is a significant amount of trivial overlap
Solution: Use overlap as a measure guide search | There is exactly one square touching the bottom of a box.
(count_equals (square (touch_bottom all_objects))
Insight: There is a significant amount of trivial overlap
Solution: Use overlap as a measure guide search | [] |
GEM-SciDuet-train-21#paper-1019#slide-7 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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GEM-SciDuet-train-21#paper-1019#slide-10 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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when trained from scratch when model initialized from an MML model trained on a seed set of offline searched paths
* using structured representations | Model does not learn without coverage! Coverage helps even with strong initialization
when trained from scratch when model initialized from an MML model trained on a seed set of offline searched paths
* using structured representations | [] |
GEM-SciDuet-train-21#paper-1019#slide-11 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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Abs. supervision + Rerank uses manually labeled abstractions of utterance - logical form pairs to get training data for a supervised system, and reranking
Our work outperforms Goldman et al., 2018 with fewer resources
* using structured representations | MaxEnt, BiAttPonter are not semantic parsers
Abs. supervision + Rerank uses manually labeled abstractions of utterance - logical form pairs to get training data for a supervised system, and reranking
Our work outperforms Goldman et al., 2018 with fewer resources
* using structured representations | [] |
GEM-SciDuet-train-21#paper-1019#slide-12 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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GEM-SciDuet-train-21#paper-1019#slide-13 | 1019 | Iterative Search for Weakly Supervised Semantic Parsing | Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates b... | {
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Iterative training: Online search with initialization MML over offline search output
Coverage during online search
SOTA single model performances: | Spuriousness is a challenge in training semantic parsers with weak supervision
Iterative training: Online search with initialization MML over offline search output
Coverage during online search
SOTA single model performances: | [] |
GEM-SciDuet-train-22#paper-1021#slide-0 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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GEM-SciDuet-train-22#paper-1021#slide-1 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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They started to talk and smile. Their parents showed up. They were happy to see them.
The brother and sister were ready for the first day of school. They were excited to go to their first day and meet new friends. They told their mom how happy they w... | The brother did not want to talk to his sister. The siblings made up.
They started to talk and smile. Their parents showed up. They were happy to see them.
The brother and sister were ready for the first day of school. They were excited to go to their first day and meet new friends. They told their mom how happy they w... | [] |
GEM-SciDuet-train-22#paper-1021#slide-2 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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BLEU, METEOR, ROUGE, CIDEr
Rennie 2017, Self-critical Sequence Training for Image Captioning | o Directly optimize the existing metrics
BLEU, METEOR, ROUGE, CIDEr
Rennie 2017, Self-critical Sequence Training for Image Captioning | [] |
GEM-SciDuet-train-22#paper-1021#slide-3 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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Fu nction Functio n Learning i (IRL) ( ) Policy Policy | R eward Reward Inverse Reinforcement Reinforcement O ptimal Optima l
Fu nction Functio n Learning i (IRL) ( ) Policy Policy | [] |
GEM-SciDuet-train-22#paper-1021#slide-4 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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GEM-SciDuet-train-22#paper-1021#slide-5 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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"Human Evaluat... | GEM-SciDuet-train-22#paper-1021#slide-5 | Policy Model | My brother recently graduated college.
CNN It was a formal cap and gown event.
My mom and dad attended.
Later, my aunt and grandma showed up.
When the event was over he even got congratulated by the mascot. | My brother recently graduated college.
CNN It was a formal cap and gown event.
My mom and dad attended.
Later, my aunt and grandma showed up.
When the event was over he even got congratulated by the mascot. | [] |
GEM-SciDuet-train-22#paper-1021#slide-6 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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Story Convolution Pooling FC layer
Kim 2014, Convolutional Neural Networks for Sentence Classification | my mom and dad attended
Story Convolution Pooling FC layer
Kim 2014, Convolutional Neural Networks for Sentence Classification | [] |
GEM-SciDuet-train-22#paper-1021#slide-7 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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"Human Evaluat... | GEM-SciDuet-train-22#paper-1021#slide-7 | Associating Reward with Story | Energy-based models associate an energy value with a sample modeling the data as a Boltzmann distribution
Approximate data distribution Partition function
Optimal reward function is achieved when
LeCun et al. 2006, A tutorial on energy-based learning | Energy-based models associate an energy value with a sample modeling the data as a Boltzmann distribution
Approximate data distribution Partition function
Optimal reward function is achieved when
LeCun et al. 2006, A tutorial on energy-based learning | [] |
GEM-SciDuet-train-22#paper-1021#slide-8 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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"Human Evaluat... | GEM-SciDuet-train-22#paper-1021#slide-8 | AREL Objective | Therefore, we define an adversarial objective with KL-divergence
Empirical distribution Policy distribution
The objective of Reward Model
The objective of Policy Model | Therefore, we define an adversarial objective with KL-divergence
Empirical distribution Policy distribution
The objective of Reward Model
The objective of Policy Model | [] |
GEM-SciDuet-train-22#paper-1021#slide-10 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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Seq2seq (Huang et al.)
HierAttRNN (Yu et al.)
ABL REUL E -(RoL urs)
Huang et al. 2016, Visual Storytelling Yu et al. 2017, Hierarchically-Attentive RNN for Album Summarization and Storytelling | Method BLEU-1 BLEU-2 BLEU-3 BLEU-4 METEOR ROUGE CIDEr
Seq2seq (Huang et al.)
HierAttRNN (Yu et al.)
ABL REUL E -(RoL urs)
Huang et al. 2016, Visual Storytelling Yu et al. 2017, Hierarchically-Attentive RNN for Album Summarization and Storytelling | [] |
GEM-SciDuet-train-22#paper-1021#slide-11 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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Relevance: the story accurately describes what is happening in the photo stream and covers the main objects.
Expressiveness: coherence, grammatically and semantically correct, no repetition, expressive language style.
Concreteness: the story should narrate concretely what is in the images r... | XE BLEU-RL CIDEr-RL GAN AREL
Relevance: the story accurately describes what is happening in the photo stream and covers the main objects.
Expressiveness: coherence, grammatically and semantically correct, no repetition, expressive language style.
Concreteness: the story should narrate concretely what is in the images r... | [] |
GEM-SciDuet-train-22#paper-1021#slide-12 | 1021 | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challe... | {
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"Human Evaluat... | GEM-SciDuet-train-22#paper-1021#slide-12 | Takeaway | o Generating and evaluating stories are both challenging due
to the complicated nature of stories
o No existing metrics are perfect for either training or testing o AREL is a better learning framework for visual storytelling
Can be applied to other generation tasks o Our approach is model-agnostic
Advanced models bette... | o Generating and evaluating stories are both challenging due
to the complicated nature of stories
o No existing metrics are perfect for either training or testing o AREL is a better learning framework for visual storytelling
Can be applied to other generation tasks o Our approach is model-agnostic
Advanced models bette... | [] |
GEM-SciDuet-train-23#paper-1024#slide-0 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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Translate a source sentence along with related nonlinguistic information
two young girls are sitting on the street eating corn .
deux jeunes filles sont assises dans la rue , mangeant du mais .
NAACL SRW 2019, Minneapolis | Practical application of machine translation
Translate a source sentence along with related nonlinguistic information
two young girls are sitting on the street eating corn .
deux jeunes filles sont assises dans la rue , mangeant du mais .
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-1 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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Statistic of training data
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Tend to output synonyms guided by language model
Source deux jeunes filles sont assises dans la rue , mangeant du mais .
Reference two young girls are sitting on the street eating corn .
NMT two y... | Multi30k [Elliott et al., 2016] has only small mount of data
Statistic of training data
Hard to train rare word translation
Tend to output synonyms guided by language model
Source deux jeunes filles sont assises dans la rue , mangeant du mais .
Reference two young girls are sitting on the street eating corn .
NMT two y... | [] |
GEM-SciDuet-train-23#paper-1024#slide-2 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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Pseudo in-domain data by filtering general domain data
Back-translation of caption/monolingual data
NAACL SRW 2019, Minneapolis | Parallel corpus without images [Elliott and Kadar, 2017; Gronroos et al., 2018]
Pseudo in-domain data by filtering general domain data
Back-translation of caption/monolingual data
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-3 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-3 | Motivation | Introduce pretrained word embedding to MMT
Improve rare word translation in MMT
Pretrained word embeddings with conventional MMT?
Pretrained Word Embedding in text-only NMT
Initialize embedding layers in encoder/decoder [Qi et al., 2018]
Improve overall performance in low-resource domain
Search-based decoder with conti... | Introduce pretrained word embedding to MMT
Improve rare word translation in MMT
Pretrained word embeddings with conventional MMT?
Pretrained Word Embedding in text-only NMT
Initialize embedding layers in encoder/decoder [Qi et al., 2018]
Improve overall performance in low-resource domain
Search-based decoder with conti... | [] |
GEM-SciDuet-train-23#paper-1024#slide-4 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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Train both MT task and shared space learning task to improve the shared encoder.
NAACL SRW 2019, Minneapolis | While validating, testing Bahdanau et al., 2015
Train both MT task and shared space learning task to improve the shared encoder.
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-5 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-5 | MMT with Embedding Prediction | 1. Use embedding prediction
While validating, testing in decoder
2. Initialize embedding layers in encoder/decoder with pretrained word embeddings
While training 3. Shift visual features to make the mean vector be a zero
NAACL SRW 2019, Minneapolis | 1. Use embedding prediction
While validating, testing in decoder
2. Initialize embedding layers in encoder/decoder with pretrained word embeddings
While training 3. Shift visual features to make the mean vector be a zero
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-6 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-6 | Embedding Prediction | i.e. Continuous Output [Kumar and Tsvetkov, 2019]
Predict a word embedding and search for the nearest word
1. Predict a word embedding of next word.
2. Compute cosine similarities with each word in pretrained word embedding.
3. Find and output the most similar word as system output.
Pretrained word embedding will NOT b... | i.e. Continuous Output [Kumar and Tsvetkov, 2019]
Predict a word embedding and search for the nearest word
1. Predict a word embedding of next word.
2. Compute cosine similarities with each word in pretrained word embedding.
3. Find and output the most similar word as system output.
Pretrained word embedding will NOT b... | [] |
GEM-SciDuet-train-23#paper-1024#slide-7 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-7 | Embedding Layer Initialization | Initialize embedding layer with pretrained word embedding
Fine-tune the embedding layer in encoder
DO NOT update the embedding layer in decoder
NAACL SRW 2019, Minneapolis | Initialize embedding layer with pretrained word embedding
Fine-tune the embedding layer in encoder
DO NOT update the embedding layer in decoder
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-8 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-8 | Loss Function | Model loss: Interpolation of each loss [Elliot and Kadaar, 2017]
MT task: Max-margin with negative sampling [Lazaridou et al., 2015]
Shared space learning task: Max-margin [Elliot and Kadaar, 2017]
NAACL SRW 2019, Minneapolis | Model loss: Interpolation of each loss [Elliot and Kadaar, 2017]
MT task: Max-margin with negative sampling [Lazaridou et al., 2015]
Shared space learning task: Max-margin [Elliot and Kadaar, 2017]
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-9 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-9 | Hubness Problem | Certain words (hubs) appear frequently in the neighbors of other words
Even of the word that has entirely no relationship with hubs
Prevent the embedding prediction model from searching for correct output words
Incorrectly output the hub word
NAACL SRW 2019, Minneapolis | Certain words (hubs) appear frequently in the neighbors of other words
Even of the word that has entirely no relationship with hubs
Prevent the embedding prediction model from searching for correct output words
Incorrectly output the hub word
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-10 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-10 | All but the Top | Address hubness problem in other NLP tasks
Debias a pretrained word embedding based on its global bias
1. Shift all word embeddings to make their mean vector into a zero vector
2. Subtract top 5 PCA components from each shifted word embedding
Applied to pretrained word embeddings for encoder/decoder
NAACL SRW 2019, Min... | Address hubness problem in other NLP tasks
Debias a pretrained word embedding based on its global bias
1. Shift all word embeddings to make their mean vector into a zero vector
2. Subtract top 5 PCA components from each shifted word embedding
Applied to pretrained word embeddings for encoder/decoder
NAACL SRW 2019, Min... | [] |
GEM-SciDuet-train-23#paper-1024#slide-11 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-11 | Implementation and Dataset | Multi30k (French to English)
Pretrained ResNet50 for visual encoder
Trained on Common Crawl and Wikipedia
Our code is here: https://github.com/toshohirasawa/nmtpytorch-emb-pred
NAACL SRW 2019, Minneapolis | Multi30k (French to English)
Pretrained ResNet50 for visual encoder
Trained on Common Crawl and Wikipedia
Our code is here: https://github.com/toshohirasawa/nmtpytorch-emb-pred
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-12 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-12 | Hyper Parameters | dimension of hidden state: 256
RNN type: GRU dimension of word embedding: 300 dimension of shared space: 2048
NAACL SRW 2019, Minneapolis | dimension of hidden state: 256
RNN type: GRU dimension of word embedding: 300 dimension of shared space: 2048
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-13 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-13 | Word level F1 score | Frequency in training data
NAACL SRW 2019, Minneapolis | Frequency in training data
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-14 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-14 | Ablation wrt Embedding Layers | Encoder Decoder Fixed BLEU METEOR
FastText FastText Yes random
Encoder/Decoder: Initialize embedding layer with random values or FastText word embedding.
Fixed (Yes/No): Whether fix the embedding layer in decoder or fine-tune that while training.
Fixing the embedding layer in decoder is essential
Keep word embeddings i... | Encoder Decoder Fixed BLEU METEOR
FastText FastText Yes random
Encoder/Decoder: Initialize embedding layer with random values or FastText word embedding.
Fixed (Yes/No): Whether fix the embedding layer in decoder or fine-tune that while training.
Fixing the embedding layer in decoder is essential
Keep word embeddings i... | [] |
GEM-SciDuet-train-23#paper-1024#slide-15 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-15 | Overall Performance | Model (+ pretrained): Apply embedding layer initialization and All-but-the-Top debiasing.
Our model performs better than baselines
Even those with embedding layer initialization
NAACL SRW 2019, Minneapolis | Model (+ pretrained): Apply embedding layer initialization and All-but-the-Top debiasing.
Our model performs better than baselines
Even those with embedding layer initialization
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-16 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-16 | Ablation wrt Visual Features | Visual Features BLEU METEOR
Visual Features (Centered/Raw/No): Use centered visual features or raw visual features to train model.
No show the result of text-only NMT with embedding prediction model.
Centering visual features is required to train our model
NAACL SRW 2019, Minneapolis | Visual Features BLEU METEOR
Visual Features (Centered/Raw/No): Use centered visual features or raw visual features to train model.
No show the result of text-only NMT with embedding prediction model.
Centering visual features is required to train our model
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-17 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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It is essential for embedding prediction model to ...
Fix the embedding in decoder
Debias the pretrained word embedding
Center the visual feature for multitask learning
Better training corpora for embedding learning in MMT domain
Incorporate visual features into contextualized... | MMT with embedding prediction improves ...
It is essential for embedding prediction model to ...
Fix the embedding in decoder
Debias the pretrained word embedding
Center the visual feature for multitask learning
Better training corpora for embedding learning in MMT domain
Incorporate visual features into contextualized... | [] |
GEM-SciDuet-train-23#paper-1024#slide-18 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-18 | Translation Example | un homme en velo pedale devant une voute .
a man on a bicycle pedals through an archway .
a man on a bicycle pedal past an arch .
Source a man on a bicycle pedals outside a monument .
IMAGINATION a man on a bicycle pedals in front of a archway .
NAACL SRW 2019, Minneapolis | un homme en velo pedale devant une voute .
a man on a bicycle pedals through an archway .
a man on a bicycle pedal past an arch .
Source a man on a bicycle pedals outside a monument .
IMAGINATION a man on a bicycle pedals in front of a archway .
NAACL SRW 2019, Minneapolis | [] |
GEM-SciDuet-train-23#paper-1024#slide-19 | 1024 | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for transla... | {
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"Visual Lat... | GEM-SciDuet-train-23#paper-1024#slide-19 | Translation Example long | quatre hommes , dont trois portent des kippas , sont assis sur un tapis a motifs bleu et vert olive .
four men , three of whom are wearing prayer caps , are sitting on a blue and olive green patterned mat .
four men , three of whom are wearing aprons , are sitting on a blue and green speedo carpet .
four men , three of... | quatre hommes , dont trois portent des kippas , sont assis sur un tapis a motifs bleu et vert olive .
four men , three of whom are wearing prayer caps , are sitting on a blue and olive green patterned mat .
four men , three of whom are wearing aprons , are sitting on a blue and green speedo carpet .
four men , three of... | [] |
GEM-SciDuet-train-24#paper-1025#slide-0 | 1025 | A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings | Recent work has managed to learn crosslingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in ... | {
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"Fully unsupervised initializa... | GEM-SciDuet-train-24#paper-1025#slide-0 | Introduction | - Cross-lingual transfer learning
- Very good results - Even better results
- Tested in favorable conditions - Fail in more challenging datasets
Previous work This work
- Adversarial learning - Self-learning
- Tested in favorable conditions - Works in challenging datasets - Fail in more challenging datasets | - Cross-lingual transfer learning
- Very good results - Even better results
- Tested in favorable conditions - Fail in more challenging datasets
Previous work This work
- Adversarial learning - Self-learning
- Tested in favorable conditions - Works in challenging datasets - Fail in more challenging datasets | [] |
GEM-SciDuet-train-24#paper-1025#slide-1 | 1025 | A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings | Recent work has managed to learn crosslingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in ... | {
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"Fully unsupervised initializa... | GEM-SciDuet-train-24#paper-1025#slide-1 | Cross lingual embedding mappings | Basque English Training dictionary
Basque arg min English
= arg min min | Basque English Training dictionary
Basque arg min English
= arg min min | [] |
GEM-SciDuet-train-24#paper-1025#slide-2 | 1025 | A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings | Recent work has managed to learn crosslingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in ... | {
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"Fully unsupervised initializa... | GEM-SciDuet-train-24#paper-1025#slide-2 | Artetxe et al ACL 2017 | = arg min min
- 25 word pairs
- num. * none 0 a | Iteration
- Numeral list A | = arg min min
- 25 word pairs
- num. * none 0 a | Iteration
- Numeral list A | [] |
GEM-SciDuet-train-24#paper-1025#slide-3 | 1025 | A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings | Recent work has managed to learn crosslingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in ... | {
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for x in vocab:
two due (two) cane (dog)
= sorted = sorted
- Stochastic dictionary induction
- Frequency-based vocabulary cutoff
- Bidirectional dictionary induction
- Final symmetric re-weighting (Artetxe et al., 2018) | 1) Fully unsupervised initialization
for x in vocab:
two due (two) cane (dog)
= sorted = sorted
- Stochastic dictionary induction
- Frequency-based vocabulary cutoff
- Bidirectional dictionary induction
- Final symmetric re-weighting (Artetxe et al., 2018) | [] |
GEM-SciDuet-train-24#paper-1025#slide-4 | 1025 | A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings | Recent work has managed to learn crosslingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in ... | {
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"Fully unsupervised initializa... | GEM-SciDuet-train-24#paper-1025#slide-4 | Experiments | 10 runs for each method
Successful runs (>5% accuracy)
Method ES-EN IT-EN TR-EN
Number of successful runs
(Hard) dataset by Dinu et al. (2016) + extensions
Supervision Method EN-IT EN-DE EN-FI EN-ES | 10 runs for each method
Successful runs (>5% accuracy)
Method ES-EN IT-EN TR-EN
Number of successful runs
(Hard) dataset by Dinu et al. (2016) + extensions
Supervision Method EN-IT EN-DE EN-FI EN-ES | [] |
GEM-SciDuet-train-24#paper-1025#slide-5 | 1025 | A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings | Recent work has managed to learn crosslingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in ... | {
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"Fully unsupervised initializa... | GEM-SciDuet-train-24#paper-1025#slide-5 | Conclusions | Not a solved problem!
More robust and accurate than previous methods
Future work: from bilingual to multilingual | Not a solved problem!
More robust and accurate than previous methods
Future work: from bilingual to multilingual | [] |
GEM-SciDuet-train-25#paper-1026#slide-0 | 1026 | SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA | We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a crosslinguistically applicable framework for semantic representation, which builds on extensive typological work and supports ra... | {
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17,
18,
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21,
22,
23,
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27,
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29,
30,
31,
32,
33,
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35,
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37,
... | {
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"2",
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"Overview",
"Task Definition",
"Data & Resources",
"TUPA: The Baseline Parser",
"Evaluation",
"·",
"Participating Systems",
"Discussion",
"Con... | GEM-SciDuet-train-25#paper-1026#slide-0 | Universal Conceptual Cognitive Annotation UCCA | Cross-linguistically applicable semantic representation (Abend and Rappoport, 2013).
Builds on Basic Linguistic Theory (R. M. W. Dixon).
Stable in translation (Sulem et al., 2015).
After graduation John moved to Paris
P D L A A
Intuitive annotation interface and guidelines (Abend et al., 2017).
The Task: UCCA parsing i... | Cross-linguistically applicable semantic representation (Abend and Rappoport, 2013).
Builds on Basic Linguistic Theory (R. M. W. Dixon).
Stable in translation (Sulem et al., 2015).
After graduation John moved to Paris
P D L A A
Intuitive annotation interface and guidelines (Abend et al., 2017).
The Task: UCCA parsing i... | [] |
GEM-SciDuet-train-25#paper-1026#slide-1 | 1026 | SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA | We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a crosslinguistically applicable framework for semantic representation, which builds on extensive typological work and supports ra... | {
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"Overview",
"Task Definition",
"Data & Resources",
"TUPA: The Baseline Parser",
"Evaluation",
"·",
"Participating Systems",
"Discussion",
"Con... | GEM-SciDuet-train-25#paper-1026#slide-1 | Applications | Machine translation (Birch et al., 2016)
Sentence splitting for text simplification (Sulem et al., 2018b).
Grammatical error correction (Choshen and Abend, 2018)
He gve an apple for john
He gave John an apple | Machine translation (Birch et al., 2016)
Sentence splitting for text simplification (Sulem et al., 2018b).
Grammatical error correction (Choshen and Abend, 2018)
He gve an apple for john
He gave John an apple | [] |
GEM-SciDuet-train-25#paper-1026#slide-2 | 1026 | SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA | We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a crosslinguistically applicable framework for semantic representation, which builds on extensive typological work and supports ra... | {
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"TUPA: The Baseline Parser",
"Evaluation",
"·",
"Participating Systems",
"Discussion",
"Con... | GEM-SciDuet-train-25#paper-1026#slide-2 | Graph Structure | Labeled directed acyclic graphs (DAGs). Complex units are non-terminal nodes. Phrases may be discontinuous.
They thought - - - remote edge
R P D A
taking a short break
Remote edges enable reentrancy. | Labeled directed acyclic graphs (DAGs). Complex units are non-terminal nodes. Phrases may be discontinuous.
They thought - - - remote edge
R P D A
taking a short break
Remote edges enable reentrancy. | [] |
GEM-SciDuet-train-25#paper-1026#slide-3 | 1026 | SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA | We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a crosslinguistically applicable framework for semantic representation, which builds on extensive typological work and supports ra... | {
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"Task Definition",
"Data & Resources",
"TUPA: The Baseline Parser",
"Evaluation",
"·",
"Participating Systems",
"Discussion",
"Con... | GEM-SciDuet-train-25#paper-1026#slide-3 | Baseline | TUPA, a transition-based UCCA parser (Hershcovich et al., 2017).
taking a short break NodeC
LSTM LSTM LSTM LSTM LSTM LSTM LSTM
They thought about taking a short break | TUPA, a transition-based UCCA parser (Hershcovich et al., 2017).
taking a short break NodeC
LSTM LSTM LSTM LSTM LSTM LSTM LSTM
They thought about taking a short break | [] |
GEM-SciDuet-train-25#paper-1026#slide-4 | 1026 | SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA | We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a crosslinguistically applicable framework for semantic representation, which builds on extensive typological work and supports ra... | {
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"Overview",
"Task Definition",
"Data & Resources",
"TUPA: The Baseline Parser",
"Evaluation",
"·",
"Participating Systems",
"Discussion",
"Con... | GEM-SciDuet-train-25#paper-1026#slide-4 | Data | English Wikipedia articles (Wiki).
English-French-German parallel corpus from
Twenty Thousand Leagues Under the Sea (20K). sentences tokens | English Wikipedia articles (Wiki).
English-French-German parallel corpus from
Twenty Thousand Leagues Under the Sea (20K). sentences tokens | [] |
GEM-SciDuet-train-25#paper-1026#slide-5 | 1026 | SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA | We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a crosslinguistically applicable framework for semantic representation, which builds on extensive typological work and supports ra... | {
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"Overview",
"Task Definition",
"Data & Resources",
"TUPA: The Baseline Parser",
"Evaluation",
"·",
"Participating Systems",
"Discussion",
"Con... | GEM-SciDuet-train-25#paper-1026#slide-5 | Tracks | French low-resource (only 15 training sentences) | French low-resource (only 15 training sentences) | [] |
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