Neural Natural Language Inference Models Enhanced with External Knowledge
Chen Qian, Xiaodan Zhu, Zhen-Hua Ling, Diana Zaiu Inkpen, Si Wei · 2018
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.