Difference between revisions of "2019 DialogueNaturalLanguageInferenc"

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* ([[2019_DialogueNaturalLanguageInferenc|Welleck et al., 2019]]) ⇒ [[author::Sean Welleck]], [[author::Jason Weston]], [[author::Arthur Szlam]], and [[author::Kyunghyun Cho]]. ([[year::2019]]). “[https://www.aclweb.org/anthology/P19-1363/ Dialogue Natural Language Inference].” In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019).  
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* ([[2019_DialogueNaturalLanguageInferenc|Welleck et al., 2019]]) [[author::Sean Welleck]], [[author::Jason Weston]], [[author::Arthur Szlam]], and [[author::Kyunghyun Cho]]. ([[year::2019]]). “[https://www.aclweb.org/anthology/P19-1363/ Dialogue Natural Language Inference].” In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019).  
  
 
<B>Subject Headings:</B> [[Natural Language Inference Task]]; [[Dialogue Natural Language Inference Task]].  
 
<B>Subject Headings:</B> [[Natural Language Inference Task]]; [[Dialogue Natural Language Inference Task]].  
  
==Notes==
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== Notes ==
 
* Pre-print(s): [https://arxiv.org/abs/1811.00671 arXiv:1811.00671]
 
* Pre-print(s): [https://arxiv.org/abs/1811.00671 arXiv:1811.00671]
==Cited By==
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== Cited By ==
 
* [[Google Scholar]]: ~5  [http://scholar.google.com/scholar?q=%222019%22+Dialogue+Natural+Language+Inference Citations]
 
* [[Google Scholar]]: ~5  [http://scholar.google.com/scholar?q=%222019%22+Dialogue+Natural+Language+Inference Citations]
 
* [[Semantic Scholar]]: ~ 5 [https://www.semanticscholar.org/paper/Dialogue-Natural-Language-Inference-Welleck-Weston/92678ba9c9a4db39d354473c826f1e21a0686007#citing-papers Citations]  
 
* [[Semantic Scholar]]: ~ 5 [https://www.semanticscholar.org/paper/Dialogue-Natural-Language-Inference-Welleck-Weston/92678ba9c9a4db39d354473c826f1e21a0686007#citing-papers Citations]  
  
==Quotes==
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== Quotes ==
  
  
===Abstract===
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=== Abstract ===
  
 
Consistency is a long standing issue faced by [[dialogue model]]s. </s>
 
Consistency is a long standing issue faced by [[dialogue model]]s. </s>
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[[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>
  
==References==
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== References ==
 
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Revision as of 01:28, 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.

References

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