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Learning natural language inference with LSTM
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.Comment: 10 pages, 2 figure
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