2018 NeuralNaturalLanguageInferenceM: Difference between revisions
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* ([[2018_NeuralNaturalLanguageInferenceM|Chen et al., 2018]]) | * ([[2018_NeuralNaturalLanguageInferenceM|Chen et al., 2018]]) ⇒ [[author::Qian Chen]], [[author::Xiaodan Zhu]], [[author::Zhen-Hua Ling]], [[author::Diana Inkpen]], and [[author::Si Wei]]. ([[year::2018]]). “[https://aclweb.org/anthology/P18-1224 Neural Natural Language Inference Models Enhanced with External Knowledge].” In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018). [http://dx.doi.org/10.18653/v1/p18-1224 doi:10.18653/v1/p18-1224] | ||
<B>Subject Headings:</B> [[Natural Language Inference Task]]; [[Neural Natural Language Inference Task]] | <B>Subject Headings:</B> [[Natural Language Inference Task]]; [[Neural Natural Language Inference Task]] | ||
==Notes== | == Notes == | ||
==Cited By== | == Cited By == | ||
* http://scholar.google.com/scholar?q=%222018%22+Neural+Natural+Language+Inference+Models+Enhanced+with+External+Knowledge | * http://scholar.google.com/scholar?q=%222018%22+Neural+Natural+Language+Inference+Models+Enhanced+with+External+Knowledge | ||
==Quotes== | == Quotes == | ||
===Abstract=== | === Abstract === | ||
[[Modeling natural language inference]] is a very challenging [[task]]. </s> | [[Modeling natural language inference]] is a very challenging [[task]]. </s> | ||
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[[We]] demonstrate that [[the proposed model]]s improve [[neural NLI model]]s to achieve the [[state-of-the-art]] [[performance]] on the [[SNLI]] and [[MultiNLI dataset]]s. </s> | [[We]] demonstrate that [[the proposed model]]s improve [[neural NLI model]]s to achieve the [[state-of-the-art]] [[performance]] on the [[SNLI]] and [[MultiNLI dataset]]s. </s> | ||
==References== | == References == | ||
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Revision as of 01:04, 13 September 2019
- (Chen et al., 2018) ⇒ Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, and Si Wei. (2018). “Neural Natural Language Inference Models Enhanced with External Knowledge.” In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018). doi:10.18653/v1/p18-1224
Subject Headings: Natural Language Inference Task; Neural Natural Language Inference Task
Notes
Cited By
Quotes
Abstract
Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2018 NeuralNaturalLanguageInferenceM | Diana Inkpen Qian Chen Xiaodan Zhu Zhen-Hua Ling Si Wei | Neural Natural Language Inference Models Enhanced with External Knowledge | 10.18653/v1/p18-1224 | 2018 |