Learning Natural Language Inference with LSTM

Understand

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.