@InProceedings{chen-EtAl:2018:Long2,
  author    = {Chen, Qian  and  Zhu, Xiaodan  and  Ling, Zhen-Hua  and  Inkpen, Diana  and  Wei, Si},
  title     = {Neural Natural Language Inference Models Enhanced with External Knowledge},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {2406--2417},
  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.},
  url       = {http://www.aclweb.org/anthology/P18-1224}
}