Difference between revisions of "2019 DialogueNaturalLanguageInferenc"

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[[In this paper]], we frame the [[consistency of dialogue agent]]s as [[natural language inference (NLI)]] and create a [[new natural language inference dataset]] called [[Dialogue NLI]]. </s>
 
[[In this paper]], we frame the [[consistency of dialogue agent]]s as [[natural language inference (NLI)]] and create a [[new natural language inference dataset]] called [[Dialogue NLI]]. </s>
 
[[We]] propose a [[method]] which demonstrates that a [[model trained]] on [[Dialogue NLI]] can be used to improve the [[consistency of a dialogue model]], and [[evaluate the method]] with [[human evaluation]] and with [[automatic metric]]s on a suite of [[evaluation set]]s designed to [[measure]] a [[dialogue model’s consistency]]. </s>
 
[[We]] propose a [[method]] which demonstrates that a [[model trained]] on [[Dialogue NLI]] can be used to improve the [[consistency of a dialogue model]], and [[evaluate the method]] with [[human evaluation]] and with [[automatic metric]]s on a suite of [[evaluation set]]s designed to [[measure]] a [[dialogue model’s consistency]]. </s>
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=== 1 Introduction ===
 
=== 1 Introduction ===
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=== 2 Dialogue Consistency and Natural Language Inference ===
 
=== 2 Dialogue Consistency and Natural Language Inference ===
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=== 3 Dialogue NLI Dataset ===
 
=== 3 Dialogue NLI Dataset ===
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==== 3.1 Triple Generation ====
 
==== 3.1 Triple Generation ====
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==== 3.2 Triple Annotation ====
 
==== 3.2 Triple Annotation ====
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==== 3.3 Statistics ====
 
==== 3.3 Statistics ====
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=== 4 Consistent Dialogue Agents Via Natural Language Inference ====
 
=== 4 Consistent Dialogue Agents Via Natural Language Inference ====
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=== 5 Experiments ===
 
=== 5 Experiments ===
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==== 5.1 Experiment 1: NLI ====
 
==== 5.1 Experiment 1: NLI ====
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==== 5.2 Experiment 2: Consistency in Dialogue ====
 
==== 5.2 Experiment 2: Consistency in Dialogue ====
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==== 5.3 Experiment 3: Human Evaluation ====
 
==== 5.3 Experiment 3: Human Evaluation ====
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=== 6 Conclusion ===
 
=== 6 Conclusion ===
  

Revision as of 04:15, 13 September 2019

Subject Headings: Natural Language Inference Task; Dialogue Natural Language Inference Task.

Notes

Cited By

Quotes

Abstract

Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model’s consistency.

1 Introduction

2 Dialogue Consistency and Natural Language Inference

3 Dialogue NLI Dataset

3.1 Triple Generation

3.2 Triple Annotation

3.3 Statistics

4 Consistent Dialogue Agents Via Natural Language Inference =

5 Experiments

5.1 Experiment 1: NLI

5.2 Experiment 2: Consistency in Dialogue

5.3 Experiment 3: Human Evaluation

6 Conclusion

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2019 DialogueNaturalLanguageInferencSean Welleck
Jason Weston
Arthur Szlam
Kyunghyun Cho
Dialogue Natural Language Inference2019