2018 NeuralNaturalLanguageInferenceM

• (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

• ACL Antology: https://www.aclweb.org/anthology/P18-1224/
• ArXiv: https://arxiv.org/abs/1711.04289

Copyright

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Cited By

• Google Scholar: ~ 27 Ciations
• Semantic Scholar: ~ 37 Citations

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