Datasets:
Ralston
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SciEvents

Dataset card Data Studio Files
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ACL
Using Context in Neural Machine Translation Training Objectives
We present Neural Machine Translation (NMT) training using document-level metrics with batch-level documents. Previous sequence-objective approaches to NMT training focus exclusively on sentence-level metrics like sentence BLEU which do not correspond to the desired evaluation metric, typically document BLEU. Meanwhile...
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2,020
[ "we present neural machine translation ( nmt ) training using document - level metrics with batch - level documents .", "previous sequence - objective approaches to nmt training focus exclusively on sentence - level metrics like sentence bleu which do not correspond to the desired evaluation metric , typically do...
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ACL
Learning Event Graph Knowledge for Abductive Reasoning
Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering. To facilitate this task, a narrative text based abductive reasoning task 𝛼NLI is proposed, together with explorati...
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2,021
[ "abductive reasoning aims at inferring the most plausible explanation for observed events , which would play critical roles in various nlp applications , such as reading comprehension and question answering .", "to facilitate this task , a narrative text based abductive reasoning task [UNK] is proposed , together...
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ACL
Glancing Transformer for Non-Autoregressive Neural Machine Translation
Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. We propose the Glancing Language Mod...
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2,021
[ "recent work on non - autoregressive neural machine translation ( nat ) aims at improving the efficiency by parallel decoding without sacrificing the quality .", "however , existing nat methods are either inferior to transformer or require multiple decoding passes , leading to reduced speedup .", "we propose th...
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ACL
Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment
Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation. In this work, we propose a simple and effective method for fine-tuning distributional word vectors for LE. Our Generalized Lexical ENtailment ...
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2,019
[ "lexical entailment ( le ; also known as hyponymy - hypernymy or is - a relation ) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation .", "in this work , we propose a simple and effective method for fine - tuning distributional word vectors for le .", "our gene...
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ACL
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond
Variational autoencoders (VAEs) combine latent variables with amortized variational inference, whose optimization usually converges into a trivial local optimum termed posterior collapse, especially in text modeling. By tracking the optimization dynamics, we observe the encoder-decoder incompatibility that leads to poo...
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[ "variational autoencoders ( vaes ) combine latent variables with amortized variational inference , whose optimization usually converges into a trivial local optimum termed posterior collapse , especially in text modeling .", "by tracking the optimization dynamics , we observe the encoder - decoder incompatibility...
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ACL
OpinionDigest: A Simple Framework for Opinion Summarization
We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extrac...
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[ "we present opiniondigest , an abstractive opinion summarization framework , which does not rely on gold - standard summaries for training .", "the framework uses an aspect - based sentiment analysis model to extract opinion phrases from reviews , and trains a transformer model to reconstruct the original reviews...
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ACL
Multi-Domain Dialogue Acts and Response Co-Generation
Generating fluent and informative responses is of critical importance for task-oriented dialogue systems. Existing pipeline approaches generally predict multiple dialogue acts first and use them to assist response generation. There are at least two shortcomings with such approaches. First, the inherent structures of mu...
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2,020
[ "generating fluent and informative responses is of critical importance for task - oriented dialogue systems .", "existing pipeline approaches generally predict multiple dialogue acts first and use them to assist response generation .", "there are at least two shortcomings with such approaches .", "first , the...
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ACL
LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution
Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. Contextual word embedding models have achieved state-of-the-art results in the lexical substitution task by relying on contextual information extracted from the replaced word within the sentence. However, such m...
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[ "lexical substitution is the task of generating meaningful substitutes for a word in a given textual context .", "contextual word embedding models have achieved state - of - the - art results in the lexical substitution task by relying on contextual information extracted from the replaced word within the sentence...
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ACL
Learning Language Specific Sub-network for Multilingual Machine Translation
Multilingual neural machine translation aims at learning a single translation model for multiple languages. These jointly trained models often suffer from performance degradationon rich-resource language pairs. We attribute this degeneration to parameter interference. In this paper, we propose LaSS to jointly train a s...
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2,021
[ "multilingual neural machine translation aims at learning a single translation model for multiple languages .", "these jointly trained models often suffer from performance degradationon rich - resource language pairs .", "we attribute this degeneration to parameter interference .", "in this paper , we propose...
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ACL
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memori...
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[ "temporal knowledge graphs ( tkgs ) have been developed and used in many different areas .", "reasoning on tkgs that predicts potential facts ( events ) in the future brings great challenges to existing models .", "when facing a prediction task , human beings usually search useful historical information ( i . e...
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ACL
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning
Encoder-decoder models for unsupervised sentence representation learning using the distributional hypothesis effectively constrain the learnt representation of a sentence to only that needed to reproduce the next sentence. While the decoder is important to constrain the representation, these models tend to discard the ...
35069245e7f012e3ad914bb97dc0933c
2,019
[ "encoder - decoder models for unsupervised sentence representation learning using the distributional hypothesis effectively constrain the learnt representation of a sentence to only that needed to reproduce the next sentence .", "while the decoder is important to constrain the representation , these models tend t...
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ACL
A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy
In recent years, reference-based and supervised summarization evaluation metrics have been widely explored. However, collecting human-annotated references and ratings are costly and time-consuming. To avoid these limitations, we propose a training-free and reference-free summarization evaluation metric. Our metric cons...
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2,021
[ "in recent years , reference - based and supervised summarization evaluation metrics have been widely explored .", "however , collecting human - annotated references and ratings are costly and time - consuming .", "to avoid these limitations , we propose a training - free and reference - free summarization eval...
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ACL
Learning to Identify Follow-Up Questions in Conversational Question Answering
Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions. Practical conversational question answering systems often receive follow-up questions in an ongoing conversation, and it is crucial for a system to be able to determine whether a question is a follow-up ...
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2,020
[ "despite recent progress in conversational question answering , most prior work does not focus on follow - up questions .", "practical conversational question answering systems often receive follow - up questions in an ongoing conversation , and it is crucial for a system to be able to determine whether a questio...
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ACL
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in both source and target languages with four components, each of which is implemented as a term interaction-based deep neural network with cros...
a49a86f25fc0f9aa890df32a9f0250cc
2,019
[ "in this paper , we propose to boost low - resource cross - lingual document retrieval performance with deep bilingual query - document representations .", "we match queries and documents in both source and target languages with four components , each of which is implemented as a term interaction - based deep neu...
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ACL
Multilingual Speech Translation from Efficient Finetuning of Pretrained Models
We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality tr...
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[ "we present a simple yet effective approach to build multilingual speech - to - text ( st ) translation through efficient transfer learning from a pretrained speech encoder and text decoder .", "our key finding is that a minimalistic lna ( layernorm and attention ) finetuning can achieve zero - shot crosslingual ...
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ACL
ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We re...
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2,021
[ "pre - trained language models ( lms ) are currently integral to many natural language processing systems .", "although multilingual lms were also introduced to serve many languages , these have limitations such as being costly at inference time and the size and diversity of non - english data involved in their p...
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ACL
Merge and Label: A Novel Neural Network Architecture for Nested NER
Named entity recognition (NER) is one of the best studied tasks in natural language processing. However, most approaches are not capable of handling nested structures which are common in many applications. In this paper we introduce a novel neural network architecture that first merges tokens and/or entities into entit...
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2,019
[ "named entity recognition ( ner ) is one of the best studied tasks in natural language processing .", "however , most approaches are not capable of handling nested structures which are common in many applications .", "in this paper we introduce a novel neural network architecture that first merges tokens and / ...
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ACL
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
Training neural models for named entity recognition (NER) in a new domain often requires additional human annotations (e.g., tens of thousands of labeled instances) that are usually expensive and time-consuming to collect. Thus, a crucial research question is how to obtain supervision in a cost-effective way. In this p...
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[ "training neural models for named entity recognition ( ner ) in a new domain often requires additional human annotations ( e . g . , tens of thousands of labeled instances ) that are usually expensive and time - consuming to collect .", "thus , a crucial research question is how to obtain supervision in a cost - ...
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ACL
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble
Although deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples. We propose Dirichlet Neighborhood Ensemble (DNE), a randomized method for training a robust model to defense synonym substitution-based attacks. During training, DNE forms virtual sentences ...
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[ "although deep neural networks have achieved prominent performance on many nlp tasks , they are vulnerable to adversarial examples .", "we propose dirichlet neighborhood ensemble ( dne ) , a randomized method for training a robust model to defense synonym substitution - based attacks .", "during training , dne ...
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ACL
Fact-based Content Weighting for Evaluating Abstractive Summarisation
Abstractive summarisation is notoriously hard to evaluate since standard word-overlap-based metrics are insufficient. We introduce a new evaluation metric which is based on fact-level content weighting, i.e. relating the facts of the document to the facts of the summary. We fol- low the assumption that a good summary w...
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[ "abstractive summarisation is notoriously hard to evaluate since standard word - overlap - based metrics are insufficient .", "we introduce a new evaluation metric which is based on fact - level content weighting , i . e . relating the facts of the document to the facts of the summary .", "we follow the assumpt...
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ACL
A Tale of a Probe and a Parser
Measuring what linguistic information is encoded in neural models of language has become popular in NLP. Researchers approach this enterprise by training “probes”—supervised models designed to extract linguistic structure from another model’s output. One such probe is the structural probe (Hewitt and Manning, 2019), de...
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[ "measuring what linguistic information is encoded in neural models of language has become popular in nlp .", "researchers approach this enterprise by training “ probes ” — supervised models designed to extract linguistic structure from another model ’ s output .", "one such probe is the structural probe ( hewit...
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ACL
STARC: Structured Annotations for Reading Comprehension
We present STARC (Structured Annotations for Reading Comprehension), a new annotation framework for assessing reading comprehension with multiple choice questions. Our framework introduces a principled structure for the answer choices and ties them to textual span annotations. The framework is implemented in OneStopQA,...
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2,020
[ "we present starc ( structured annotations for reading comprehension ) , a new annotation framework for assessing reading comprehension with multiple choice questions .", "our framework introduces a principled structure for the answer choices and ties them to textual span annotations .", "the framework is imple...
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ACL
Self-Regulated Interactive Sequence-to-Sequence Learning
Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost...
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2,019
[ "not all types of supervision signals are created equal : different types of feedback have different costs and effects on learning .", "we show how self - regulation strategies that decide when to ask for which kind of feedback from a teacher ( or from oneself ) can be cast as a learning - to - learn problem lead...
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ACL
Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification
Aspect-based sentiment classification is a popular task aimed at identifying the corresponding emotion of a specific aspect. One sentence may contain various sentiments for different aspects. Many sophisticated methods such as attention mechanism and Convolutional Neural Networks (CNN) have been widely employed for han...
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[ "aspect - based sentiment classification is a popular task aimed at identifying the corresponding emotion of a specific aspect .", "one sentence may contain various sentiments for different aspects .", "many sophisticated methods such as attention mechanism and convolutional neural networks ( cnn ) have been wi...
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ACL
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension
Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the tar...
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[ "multilingual pre - trained models are able to zero - shot transfer knowledge from rich - resource to low - resource languages in machine reading comprehension ( mrc ) .", "however , inherent linguistic discrepancies in different languages could make answer spans predicted by zero - shot transfer violate syntacti...
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ACL
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external kno...
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[ "open - domain code generation aims to generate code in a general - purpose programming language ( such as python ) from natural language ( nl ) intents .", "motivated by the intuition that developers usually retrieve resources on the web when writing code , we explore the effectiveness of incorporating two varie...
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ACL
SENT: Sentence-level Distant Relation Extraction via Negative Training
Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications that need the exact relation type. Directly using bag labels for sentence-level training will introduce much noise, thus severely degrading p...
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[ "distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag , while accurate sentence labels are important for downstream applications that need the exact relation type .", "directly using bag labels for sentence - level training will introduce much noise , thus sever...
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ACL
RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization
A release note is a technical document that describes the latest changes to a software product and is crucial in open source software development. However, it still remains challenging to generate release notes automatically. In this paper, we present a new dataset called RNSum, which contains approximately 82,000 Engl...
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[ "a release note is a technical document that describes the latest changes to a software product and is crucial in open source software development .", "however , it still remains challenging to generate release notes automatically .", "in this paper , we present a new dataset called rnsum , which contains appro...
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ACL
Biomedical Entity Representations with Synonym Marginalization
Biomedical named entities often play important roles in many biomedical text mining tools. However, due to the incompleteness of provided synonyms and numerous variations in their surface forms, normalization of biomedical entities is very challenging. In this paper, we focus on learning representations of biomedical e...
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[ "biomedical named entities often play important roles in many biomedical text mining tools .", "however , due to the incompleteness of provided synonyms and numerous variations in their surface forms , normalization of biomedical entities is very challenging .", "in this paper , we focus on learning representat...
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ACL
CDRNN: Discovering Complex Dynamics in Human Language Processing
The human mind is a dynamical system, yet many analysis techniques used to study it are limited in their ability to capture the complex dynamics that may characterize mental processes. This study proposes the continuous-time deconvolutional regressive neural network (CDRNN), a deep neural extension of continuous-time d...
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[ "the human mind is a dynamical system , yet many analysis techniques used to study it are limited in their ability to capture the complex dynamics that may characterize mental processes .", "this study proposes the continuous - time deconvolutional regressive neural network ( cdrnn ) , a deep neural extension of ...
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ACL
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the ...
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[ "distance based knowledge graph embedding methods show promising results on link prediction task , on which two topics have been widely studied : one is the ability to handle complex relations , such as n - to - 1 , 1 - to - n and n - to - n , the other is to encode various relation patterns , such as symmetry / an...
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ACL
Pre-Learning Environment Representations for Data-Efficient Neural Instruction Following
We consider the problem of learning to map from natural language instructions to state transitions (actions) in a data-efficient manner. Our method takes inspiration from the idea that it should be easier to ground language to concepts that have already been formed through pre-linguistic observation. We augment a basel...
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[ "we consider the problem of learning to map from natural language instructions to state transitions ( actions ) in a data - efficient manner .", "our method takes inspiration from the idea that it should be easier to ground language to concepts that have already been formed through pre - linguistic observation ."...
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ACL
GLUECoS: An Evaluation Benchmark for Code-Switched NLP
Code-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on cross-lingual and multilingual tasks. We present an evaluation benchmark, GLUECoS, for code-switched lan...
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[ "code - switching is the use of more than one language in the same conversation or utterance .", "recently , multilingual contextual embedding models , trained on multiple monolingual corpora , have shown promising results on cross - lingual and multilingual tasks .", "we present an evaluation benchmark , gluec...
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ACL
The AI Doctor Is In: A Survey of Task-Oriented Dialogue Systems for Healthcare Applications
Task-oriented dialogue systems are increasingly prevalent in healthcare settings, and have been characterized by a diverse range of architectures and objectives. Although these systems have been surveyed in the medical community from a non-technical perspective, a systematic review from a rigorous computational perspec...
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[ "task - oriented dialogue systems are increasingly prevalent in healthcare settings , and have been characterized by a diverse range of architectures and objectives .", "although these systems have been surveyed in the medical community from a non - technical perspective , a systematic review from a rigorous comp...
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ACL
Multi-Task Networks with Universe, Group, and Task Feature Learning
We present methods for multi-task learning that take advantage of natural groupings of related tasks. Task groups may be defined along known properties of the tasks, such as task domain or language. Such task groups represent supervised information at the inter-task level and can be encoded into the model. We investiga...
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[ "we present methods for multi - task learning that take advantage of natural groupings of related tasks .", "task groups may be defined along known properties of the tasks , such as task domain or language .", "such task groups represent supervised information at the inter - task level and can be encoded into t...
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ACL
Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems
Developing Video-Grounded Dialogue Systems (VGDS), where a dialogue is conducted based on visual and audio aspects of a given video, is significantly more challenging than traditional image or text-grounded dialogue systems because (1) feature space of videos span across multiple picture frames, making it difficult to ...
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[ "developing video - grounded dialogue systems ( vgds ) , where a dialogue is conducted based on visual and audio aspects of a given video , is significantly more challenging than traditional image or text - grounded dialogue systems because ( 1 ) feature space of videos span across multiple picture frames , making ...
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ACL
On the Sensitivity and Stability of Model Interpretations in NLP
Recent years have witnessed the emergence of a variety of post-hoc interpretations that aim to uncover how natural language processing (NLP) models make predictions. Despite the surge of new interpretation methods, it remains an open problem how to define and quantitatively measure the faithfulness of interpretations, ...
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[ "recent years have witnessed the emergence of a variety of post - hoc interpretations that aim to uncover how natural language processing ( nlp ) models make predictions .", "despite the surge of new interpretation methods , it remains an open problem how to define and quantitatively measure the faithfulness of i...
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ACL
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation that combines a pretrained language model with “expert” LMs and/or “anti-expert” LMs in a product of e...
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[ "despite recent advances in natural language generation , it remains challenging to control attributes of generated text .", "we propose dexperts : decoding - time experts , a decoding - time method for controlled text generation that combines a pretrained language model with “ expert ” lms and / or “ anti - expe...
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ACL
Training Neural Machine Translation to Apply Terminology Constraints
This paper proposes a novel method to inject custom terminology into neural machine translation at run time. Previous works have mainly proposed modifications to the decoding algorithm in order to constrain the output to include run-time-provided target terms. While being effective, these constrained decoding methods a...
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ACL
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with analysis of the TQA dataset. First, solving the TQA problems requires to comprehend multi-modal contex...
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ACL
Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis
Cognitive task analysis (CTA) is a type of analysis in applied psychology aimed at eliciting and representing the knowledge and thought processes of domain experts. In CTA, often heavy human labor is involved to parse the interview transcript into structured knowledge (e.g., flowchart for different actions). To reduce ...
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ACL
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation
Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift. Recent work has tied these shortcomings to beam search – the de facto standard inference algorithm in ...
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ACL
Understanding Iterative Revision from Human-Written Text
Writing is, by nature, a strategic, adaptive, and, more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revision have focused on defining edit intention taxonomies within a single domain or developing computational models with a single level of edit ...
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ACL
The Summary Loop: Learning to Write Abstractive Summaries Without Examples
This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint. It introduces a novel method that encourages the inclusion of key terms from the original document into the summary: key terms are masked out of the origin...
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ACL
Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction
We present a study on leveraging multilingual pre-trained generative language models for zero-shot cross-lingual event argument extraction (EAE). By formulating EAE as a language generation task, our method effectively encodes event structures and captures the dependencies between arguments. We design language-agnostic...
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[ "we present a study on leveraging multilingual pre - trained generative language models for zero - shot cross - lingual event argument extraction ( eae ) .", "by formulating eae as a language generation task , our method effectively encodes event structures and captures the dependencies between arguments .", "w...
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ACL
Nibbling at the Hard Core of Word Sense Disambiguation
With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained language models. And yet, i...
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[ "with state - of - the - art systems having finally attained estimated human performance , word sense disambiguation ( wsd ) has now joined the array of natural language processing tasks that have seemingly been solved , thanks to the vast amounts of knowledge encoded into transformer - based pre - trained language...
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ACL
SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures
Current open-domain conversational models can easily be made to talk in inadequate ways. Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of these safety failures. However, current state-of-the-art models tend ...
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ACL
Compression of Generative Pre-trained Language Models via Quantization
The increasing size of generative Pre-trained Language Models (PLMs) have greatly increased the demand for model compression. Despite various methods to compress BERT or its variants, there are few attempts to compress generative PLMs, and the underlying difficulty remains unclear. In this paper, we compress generative...
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2,022
[ "the increasing size of generative pre - trained language models ( plms ) have greatly increased the demand for model compression .", "despite various methods to compress bert or its variants , there are few attempts to compress generative plms , and the underlying difficulty remains unclear .", "in this paper ...
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ACL
Do Transformers Need Deep Long-Range Memory?
Deep attention models have advanced the modelling of sequential data across many domains. For language modelling in particular, the Transformer-XL — a Transformer augmented with a long-range memory of past activations — has been shown to be state-of-the-art across a variety of well-studied benchmarks. The Transformer-X...
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2,020
[ "deep attention models have advanced the modelling of sequential data across many domains .", "for language modelling in particular , the transformer - xl — a transformer augmented with a long - range memory of past activations — has been shown to be state - of - the - art across a variety of well - studied bench...
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ACL
Automated Crossword Solving
We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving crossword puzzles. Our system works by generating answer candidates for each crossword clue using neural question answering models and then combines loopy belief propagation with local search to find full puzzle solutions. C...
0006b7d827ac773b6c89eab622928e25
2,022
[ "we present the berkeley crossword solver , a state - of - the - art approach for automatically solving crossword puzzles .", "our system works by generating answer candidates for each crossword clue using neural question answering models and then combines loopy belief propagation with local search to find full p...
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ACL
Multi-Hypothesis Machine Translation Evaluation
Reliably evaluating Machine Translation (MT) through automated metrics is a long-standing problem. One of the main challenges is the fact that multiple outputs can be equally valid. Attempts to minimise this issue include metrics that relax the matching of MT output and reference strings, and the use of multiple refere...
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2,020
[ "reliably evaluating machine translation ( mt ) through automated metrics is a long - standing problem .", "one of the main challenges is the fact that multiple outputs can be equally valid .", "attempts to minimise this issue include metrics that relax the matching of mt output and reference strings , and the ...
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ACL
Collaborative Dialogue in Minecraft
We wish to develop interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios. Since computer games allow us to simulate such tasks without the need for physical robots, we define a Minecraft-based collaborative building task in which one player (A, the Architect) is shown ...
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2,019
[ "we wish to develop interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios .", "since computer games allow us to simulate such tasks without the need for physical robots , we define a minecraft - based collaborative building task in which one player ( a , the arch...
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ACL
Learning to Generate Task-Specific Adapters from Task Description
Pre-trained text-to-text transformers such as BART have achieved impressive performance across a range of NLP tasks. Recent study further shows that they can learn to generalize to novel tasks, by including task descriptions as part of the source sequence and training the model with (source, target) examples. At test t...
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2,021
[ "pre - trained text - to - text transformers such as bart have achieved impressive performance across a range of nlp tasks .", "recent study further shows that they can learn to generalize to novel tasks , by including task descriptions as part of the source sequence and training the model with ( source , target ...
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ACL
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records
De-identification is the task of detecting protected health information (PHI) in medical text. It is a critical step in sanitizing electronic health records (EHR) to be shared for research. Automatic de-identification classifiers can significantly speed up the sanitization process. However, obtaining a large and divers...
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2,019
[ "de - identification is the task of detecting protected health information ( phi ) in medical text .", "it is a critical step in sanitizing electronic health records ( ehr ) to be shared for research .", "automatic de - identification classifiers can significantly speed up the sanitization process .", "howeve...
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ACL
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation
A common way to combat exposure bias is by applying scores from evaluation metrics as rewards in reinforcement learning (RL). Metrics leveraging contextualized embeddings appear more flexible than their n-gram matching counterparts and thus ideal as training rewards. However, metrics such as BERTScore greedily align ca...
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2,022
[ "a common way to combat exposure bias is by applying scores from evaluation metrics as rewards in reinforcement learning ( rl ) .", "metrics leveraging contextualized embeddings appear more flexible than their n - gram matching counterparts and thus ideal as training rewards .", "however , metrics such as berts...
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ACL
Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction
We investigate the problem of Chinese Grammatical Error Correction (CGEC) and present a new framework named Tail-to-Tail (TtT) non-autoregressive sequence prediction to address the deep issues hidden in CGEC. Considering that most tokens are correct and can be conveyed directly from source to target, and the error posi...
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2,021
[ "we investigate the problem of chinese grammatical error correction ( cgec ) and present a new framework named tail - to - tail ( ttt ) non - autoregressive sequence prediction to address the deep issues hidden in cgec .", "considering that most tokens are correct and can be conveyed directly from source to targe...
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ACL
Automatic Detection of Generated Text is Easiest when Humans are Fooled
Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. The capabilities of humans and automatic discriminators to detect machine-generated text have been a large source of research interest, but humans and machines rely on different cues to make their ...
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2,020
[ "recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human - sounding text .", "the capabilities of humans and automatic discriminators to detect machine - generated text have been a large source of research interest , but humans and machines rely on different cue...
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ACL
Low-resource Deep Entity Resolution with Transfer and Active Learning
Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. It is a key step for knowledge base creation and text mining. Recent adaptation of deep learning methods for ER mitigates the need for dataset-specific feature engineering by constructing distri...
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2,019
[ "entity resolution ( er ) is the task of identifying different representations of the same real - world entities across databases .", "it is a key step for knowledge base creation and text mining .", "recent adaptation of deep learning methods for er mitigates the need for dataset - specific feature engineering...
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ACL
ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding
The goal of Knowledge graph embedding (KGE) is to learn how to represent the low dimensional vectors for entities and relations based on the observed triples. The conventional shallow models are limited to their expressiveness. ConvE (Dettmers et al., 2018) takes advantage of CNN and improves the expressive power with ...
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2,020
[ "the goal of knowledge graph embedding ( kge ) is to learn how to represent the low dimensional vectors for entities and relations based on the observed triples .", "the conventional shallow models are limited to their expressiveness .", "conve ( dettmers et al . , 2018 ) takes advantage of cnn and improves the...
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ACL
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer
Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to mo...
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2,021
[ "disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer .", "these techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence .", "in this paper , we propose a counterfactual ...
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ACL
Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction
Continual learning has gained increasing attention in recent years, thanks to its biological interpretation and efficiency in many real-world applications. As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different r...
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2,021
[ "continual learning has gained increasing attention in recent years , thanks to its biological interpretation and efficiency in many real - world applications .", "as a typical task of continual learning , continual relation extraction ( cre ) aims to extract relations between entities from texts , where the samp...
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ACL
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution
We consider the task of document-level entity linking (EL), where it is important to make consistent decisions for entity mentions over the full document jointly. We aim to leverage explicit “connections” among mentions within the document itself: we propose to join EL and coreference resolution (coref) in a single str...
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2,022
[ "we consider the task of document - level entity linking ( el ) , where it is important to make consistent decisions for entity mentions over the full document jointly .", "we aim to leverage explicit “ connections ” among mentions within the document itself : we propose to join el and coreference resolution ( co...
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ACL
Multi-task Pairwise Neural Ranking for Hashtag Segmentation
Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently in...
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2,019
[ "hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability , aiding search , or providing additional semantics .", "however , the semantic content of hashtags is not straightforward to infer as these represent ad - hoc conventions whi...
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ACL
A2N: Attending to Neighbors for Knowledge Graph Inference
State-of-the-art models for knowledge graph completion aim at learning a fixed embedding representation of entities in a multi-relational graph which can generalize to infer unseen entity relationships at test time. This can be sub-optimal as it requires memorizing and generalizing to all possible entity relationships ...
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2,019
[ "state - of - the - art models for knowledge graph completion aim at learning a fixed embedding representation of entities in a multi - relational graph which can generalize to infer unseen entity relationships at test time .", "this can be sub - optimal as it requires memorizing and generalizing to all possible ...
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ACL
Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation
We study the task of response generation. Conventional methods employ a fixed vocabulary and one-pass decoding, which not only make them prone to safe and general responses but also lack further refining to the first generated raw sequence. To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN)...
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2,019
[ "we study the task of response generation .", "conventional methods employ a fixed vocabulary and one - pass decoding , which not only make them prone to safe and general responses but also lack further refining to the first generated raw sequence .", "to tackle the above two problems , we present a vocabulary ...
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ACL
BERT Rediscovers the Classical NLP Pipeline
Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, ...
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2,019
[ "pre - trained text encoders have rapidly advanced the state of the art on many nlp tasks .", "we focus on one such model , bert , and aim to quantify where linguistic information is captured within the network .", "we find that the model represents the steps of the traditional nlp pipeline in an interpretable ...
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ACL
NNE: A Dataset for Nested Named Entity Recognition in English Newswire
Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks. However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available in nested entity mentions. We describe NNE—a fine-grained, nested named entity dataset over t...
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2,019
[ "named entity recognition ( ner ) is widely used in natural language processing applications and downstream tasks .", "however , most ner tools target flat annotation from popular datasets , eschewing the semantic information available in nested entity mentions .", "we describe nne — a fine - grained , nested n...
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ACL
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms. Previous work focuses on automatic commenting based solely on textual content. However, in real-scenarios, online articles usually contain multiple mo...
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2,019
[ "automatic commenting of online articles can provide additional opinions and facts to the reader , which improves user experience and engagement on social media platforms .", "previous work focuses on automatic commenting based solely on textual content .", "however , in real - scenarios , online articles usual...
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ACL
A Cross-Domain Transferable Neural Coherence Model
Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, becau...
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2,019
[ "coherence is an important aspect of text quality and is crucial for ensuring its readability .", "one important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text .", "previous work advocates for generative models for cross - domain ge...
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ACL
Let Me Choose: From Verbal Context to Font Selection
In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to. Compared to related work leveraging the surrounding visual context, we choose to focus only on the input text, which can enable new applications for which the text is the onl...
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2,020
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ACL
Maria: A Visual Experience Powered Conversational Agent
Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence. Image-grounded conversation is thus proposed to address this challenge. Existing works focus on exploring the multimodal dialog models that ground the conversation on a given imag...
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2,021
[ "arguably , the visual perception of conversational agents to the physical world is a key way for them to exhibit the human - like intelligence .", "image - grounded conversation is thus proposed to address this challenge .", "existing works focus on exploring the multimodal dialog models that ground the conver...
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ACL
Data Programming for Learning Discourse Structure
This paper investigates the advantages and limits of data programming for the task of learning discourse structure. The data programming paradigm implemented in the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the “generative step” into probabil...
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2,019
[ "this paper investigates the advantages and limits of data programming for the task of learning discourse structure .", "the data programming paradigm implemented in the snorkel framework allows a user to label training data using expert - composed heuristics , which are then transformed via the “ generative step...
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ACL
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators
Prompting has recently been shown as a promising approach for applying pre-trained language models to perform downstream tasks. We present Multi-Stage Prompting, a simple and automatic approach for leveraging pre-trained language models to translation tasks. To better mitigate the discrepancy between pre-training and t...
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[ "prompting has recently been shown as a promising approach for applying pre - trained language models to perform downstream tasks .", "we present multi - stage prompting , a simple and automatic approach for leveraging pre - trained language models to translation tasks .", "to better mitigate the discrepancy be...
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ACL
Evaluating Explanation Methods for Neural Machine Translation
Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods. Word Alignment Error Rate can be used as such a metric that matches human understanding, however, it can not measure explanation methods on those target words t...
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2,020
[ "recently many efforts have been devoted to interpreting the black - box nmt models , but little progress has been made on metrics to evaluate explanation methods .", "word alignment error rate can be used as such a metric that matches human understanding , however , it can not measure explanation methods on thos...
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ACL
Predicting Degrees of Technicality in Automatic Terminology Extraction
While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied. We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on techni...
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2,020
[ "while automatic term extraction is a well - researched area , computational approaches to distinguish between degrees of technicality are still understudied .", "we semi - automatically create a german gold standard of technicality across four domains , and illustrate the impact of a web - crawled general - lang...
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ACL
A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation
Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images. However, dominant multi-modal NMT models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have potential to refine multi-modal repres...
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[ "multi - modal neural machine translation ( nmt ) aims to translate source sentences into a target language paired with images .", "however , dominant multi - modal nmt models do not fully exploit fine - grained semantic correspondences between semantic units of different modalities , which have potential to refi...
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ACL
Just Rank: Rethinking Evaluation with Word and Sentence Similarities
Word and sentence embeddings are useful feature representations in natural language processing. However, intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade. Word and sentence similarity tasks have become the de facto evaluation method. It leads models to ...
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2,022
[ "word and sentence embeddings are useful feature representations in natural language processing .", "however , intrinsic evaluation for embeddings lags far behind , and there has been no significant update since the past decade .", "word and sentence similarity tasks have become the de facto evaluation method ....
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ACL
Multi-Task Retrieval for Knowledge-Intensive Tasks
Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditional methods like tf-idf and BM25, its performance degrades considerably when applied to out-of-domain data. Driven by the question of wheth...
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[ "retrieving relevant contexts from a large corpus is a crucial step for tasks such as open - domain question answering and fact checking .", "although neural retrieval outperforms traditional methods like tf - idf and bm25 , its performance degrades considerably when applied to out - of - domain data .", "drive...
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ACL
Factorising Meaning and Form for Intent-Preserving Paraphrasing
We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form. Our model combines a careful choice of training objective with a principled information bottleneck, to induce a latent encoding space that disentangles meaning and form. We train an enco...
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[ "we propose a method for generating paraphrases of english questions that retain the original intent but use a different surface form .", "our model combines a careful choice of training objective with a principled information bottleneck , to induce a latent encoding space that disentangles meaning and form .", ...
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ACL
Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter
We present a new challenging stance detection dataset, called Will-They-Won’t-They (WT--WT), which contains 51,284 tweets in English, making it by far the largest available dataset of the type. All the annotations are carried out by experts; therefore, the dataset constitutes a high-quality and reliable benchmark for f...
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ACL
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System
Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. In the practical application, a reliable dialogue system should know what it does not know. In this paper, we introduce a new task, Novel Slot Detection (NSD), in the task-oriented dialogue system. NSD aims to disc...
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[ "existing slot filling models can only recognize pre - defined in - domain slot types from a limited slot set .", "in the practical application , a reliable dialogue system should know what it does not know .", "in this paper , we introduce a new task , novel slot detection ( nsd ) , in the task - oriented dial...
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ACL
HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding
The International Classification of Diseases (ICD) provides a standardized way for classifying diseases, which endows each disease with a unique code. ICD coding aims to assign proper ICD codes to a medical record. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automa...
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2,020
[ "the international classification of diseases ( icd ) provides a standardized way for classifying diseases , which endows each disease with a unique code .", "icd coding aims to assign proper icd codes to a medical record .", "since manual coding is very laborious and prone to errors , many methods have been pr...
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ACL
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings
It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR). An important property of the implicit connectives is that they can be accurately mapped into the discourse relations conveying their functions. In this work, we explo...
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ACL
Towards Comprehensive Description Generation from Factual Attribute-value Tables
The comprehensive descriptions for factual attribute-value tables, which should be accurate, informative and loyal, can be very helpful for end users to understand the structured data in this form. However previous neural generators might suffer from key attributes missing, less informative and groundless information p...
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[ "the comprehensive descriptions for factual attribute - value tables , which should be accurate , informative and loyal , can be very helpful for end users to understand the structured data in this form .", "however previous neural generators might suffer from key attributes missing , less informative and groundl...
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ACL
Matching the Blanks: Distributional Similarity for Relation Learning
General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph. H...
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2,019
[ "general purpose relation extractors , which can model arbitrary relations , are a core aspiration in information extraction .", "efforts have been made to build general purpose extractors that represent relations with their surface forms , or which jointly embed surface forms with relations from an existing know...
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ACL
Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking
In dialogue state tracking, dialogue history is a crucial material, and its utilization varies between different models. However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated. Appare...
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[ "in dialogue state tracking , dialogue history is a crucial material , and its utilization varies between different models .", "however , no matter how the dialogue history is used , each existing model uses its own consistent dialogue history during the entire state tracking process , regardless of which slot is...
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ACL
Balancing Training for Multilingual Neural Machine Translation
When training multilingual machine translation (MT) models that can translate to/from multiple languages, we are faced with imbalanced training sets: some languages have much more training data than others. Standard practice is to up-sample less resourced languages to increase representation, and the degree of up-sampl...
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2,020
[ "when training multilingual machine translation ( mt ) models that can translate to / from multiple languages , we are faced with imbalanced training sets : some languages have much more training data than others .", "standard practice is to up - sample less resourced languages to increase representation , and th...
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ACL
Politeness Transfer: A Tag and Generate Approach
This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and gen...
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[ "this paper introduces a new task of politeness transfer which involves converting non - polite sentences to polite sentences while preserving the meaning .", "we also provide a dataset of more than 1 . 39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task .", "we...
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ACL
Discrete Latent Variable Representations for Low-Resource Text Classification
While much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically more space efficient. We consider several approaches to learning discrete latent variable models for text in the case where exact marginali...
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2,020
[ "while much work on deep latent variable models of text uses continuous latent variables , discrete latent variables are interesting because they are more interpretable and typically more space efficient .", "we consider several approaches to learning discrete latent variable models for text in the case where exa...
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ACL
Collocation Classification with Unsupervised Relation Vectors
Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words. In this paper, we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations, i.e., pairs of words between whi...
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2,019
[ "lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words .", "in this paper , we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations , i . e . , pairs of ...
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ACL
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements. However, most of the neural MRC models are still far from robust and fail to generalize well in real-world applications. In order to comprehensively verify the robustness and generalization of ...
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[ "machine reading comprehension ( mrc ) is a crucial task in natural language processing and has achieved remarkable advancements .", "however , most of the neural mrc models are still far from robust and fail to generalize well in real - world applications .", "in order to comprehensively verify the robustness ...
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ACL
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
Recent research demonstrates the effectiveness of using fine-tuned language models (LM) for dense retrieval. However, dense retrievers are hard to train, typically requiring heavily engineered fine-tuning pipelines to realize their full potential. In this paper, we identify and address two underlying problems of dense ...
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ACL
Learning Dense Representations of Phrases at Scale
Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019). However, current phrase retrieval models heavily depend on sparse representations and still underperform retriever-reader approaches. In this work, w...
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2,021
[ "open - domain question answering can be reformulated as a phrase retrieval problem , without the need for processing documents on - demand during inference ( seo et al . , 2019 ) .", "however , current phrase retrieval models heavily depend on sparse representations and still underperform retriever - reader appr...
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ACL
Challenges and Strategies in Cross-Cultural NLP
Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although l...
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2,022
[ "various efforts in the natural language processing ( nlp ) community have been made to accommodate linguistic diversity and serve speakers of many different languages .", "however , it is important to acknowledge that speakers and the content they produce and require , vary not just by language , but also by cul...
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ACL
LeeBERT: Learned Early Exit for BERT with cross-level optimization
Pre-trained language models like BERT are performant in a wide range of natural language tasks. However, they are resource exhaustive and computationally expensive for industrial scenarios. Thus, early exits are adopted at each layer of BERT to perform adaptive computation by predicting easier samples with the first fe...
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[ "pre - trained language models like bert are performant in a wide range of natural language tasks .", "however , they are resource exhaustive and computationally expensive for industrial scenarios .", "thus , early exits are adopted at each layer of bert to perform adaptive computation by predicting easier samp...
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ACL
Improving Disfluency Detection by Self-Training a Self-Attentive Model
Self-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detection in speech transcripts. Since the contextualized word embeddings are pre-trained on a large amount of unlabeled data, using additional unla...
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2,020
[ "self - attentive neural syntactic parsers using contextualized word embeddings ( e . g . elmo or bert ) currently produce state - of - the - art results in joint parsing and disfluency detection in speech transcripts .", "since the contextualized word embeddings are pre - trained on a large amount of unlabeled d...
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ACL
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning
Recently, finetuning a pretrained language model to capture the similarity between sentence embeddings has shown the state-of-the-art performance on the semantic textual similarity (STS) task. However, the absence of an interpretation method for the sentence similarity makes it difficult to explain the model output. In...
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2,022
[ "recently , finetuning a pretrained language model to capture the similarity between sentence embeddings has shown the state - of - the - art performance on the semantic textual similarity ( sts ) task .", "however , the absence of an interpretation method for the sentence similarity makes it difficult to explain...
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ACL
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction
In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there is a lack of training data. On the one hand, PAIE utilizes prompt tuning for extractive objectives to take the best advantages of Pre-trained...
aa8dcb455038ef2a2a744c58a1972d2e
2,022
[ "in this paper , we propose an effective yet efficient model paie for both sentence - level and document - level event argument extraction ( eae ) , which also generalizes well when there is a lack of training data .", "on the one hand , paie utilizes prompt tuning for extractive objectives to take the best advan...
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ACL
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network
Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating a redundant slot value. Prior works refer this to hallucination phenomenon. In this paper, we...
24f9be0c5791a1ad22b59b6ddd62b825
2,020
[ "data - driven approaches using neural networks have achieved promising performances in natural language generation ( nlg ) .", "however , neural generators are prone to make mistakes , e . g . , neglecting an input slot value and generating a redundant slot value .", "prior works refer this to hallucination ph...
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ACL
Zero-Shot Entity Linking by Reading Entity Descriptions
We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. The goal is to enable robust transfer to highly specialized domains, and so no metadata or alias tables are assumed. In this setting, entities are only identified by text descriptions, and mode...
ba6095d846d233ba05c7fb89a50343f3
2,019
[ "we present the zero - shot entity linking task , where mentions must be linked to unseen entities without in - domain labeled data .", "the goal is to enable robust transfer to highly specialized domains , and so no metadata or alias tables are assumed .", "in this setting , entities are only identified by tex...
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