Reading and thinking: Re-read LSTM unit for textual entailment recognition

Recognizing Textual Entailment (RTE) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI¹) corpus has made it possible to develop and evaluate deep neural network methods for the RTE task. Previous neural network based methods usually try to encode the two sentences and send them together into multi-layer perceptron, or use LSTM-RNN to link two sentence together while using attention mechanic to enhance the model's ability. In this paper, we propose to use the intensive reading mechanic, which means to re-read the sentence (read the sentence again) according to the memory of the other sentence for a better understanding of the sentence pair. The re-read process can be applied alternatively between the two sentences. Experiments show that we achieve results better than current state-of-art equivalents.