Conference Proceeding Article
Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI). In this paper, we propose a special long short-term memory (LSTM) architecture for NLI. Our model builds on top of a recently proposed neural attention model for NLI but is based on a significantly different idea. Instead of deriving sentence embeddings for the premise and the hypothesis to be used for classification, our solution uses a match-LSTM to perform word-by-word matching of the hypothesis with the premise. This LSTM is able to place more emphasis on important word-level matching results. In particular, we observe that this LSTM remembers important mismatches that are critical for predicting the contradiction or the neutral relationship label. On the SNLI corpus, our model achieves an accuracy of 86.1%, outperforming the state of the art.
Databases and Information Systems | Systems Architecture
Data Science and Engineering
NAACL HLT 2016: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: San Diego, California, 2016 June 12-17
Association for Computational Linguistics (ACL)
City or Country
WANG, Shuohang and JIANG, Jing.
Learning natural language inference with LSTM. (2016). NAACL HLT 2016: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: San Diego, California, 2016 June 12-17. 1442-1451. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3434
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.