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arXiv · 1711.04289

Neural Natural Language Inference Models Enhanced with External Knowledge

Qian Chen·Xiaodan Zhu·Zhen-Hua Ling·Diana Inkpen·Si Wei
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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.

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Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, Si Wei. 2018-06-23. Neural Natural Language Inference Models Enhanced with External Knowledge. https://doi.org/10.18653/v1%2Fp18-1224

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