Learning Natural Language Inference with LSTM

TL;DR

This paper introduces a novel match-LSTM architecture for natural language inference that performs word-by-word matching between premise and hypothesis, achieving state-of-the-art accuracy on the SNLI dataset.

Contribution

It presents a new LSTM-based model that emphasizes important word matches without relying on fixed sentence embeddings, improving NLI performance.

Findings

Achieved 86.1% accuracy on SNLI dataset.

Outperformed previous state-of-the-art models.

Effectively captures critical mismatches for inference.

Abstract

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.