DGI: Recognition of Textual Entailment via dynamic gate Matching

摘要

Recognizing Textual Entailment (RTE) is an integral part of intelligent machines which is able to understand and reason with natural languages. Some special embedding methods such as the attention mechanism exploit the semantic information without considering the features of sentence interaction, which also affect the word-level attention weight when a word appears at multiple positions of a sentence. In this study, we propose a Dynamic Gate Inference model (DGI) to fulfill the RTE task. In the DGI model, different aspects of semantic information are extracted from a premise sentence and a hypothesis sentence by a proposed dynamic gate Matching LSTM structure (gMatch), which combines the word-level fine-grained reasoning mechanism with the sentence-level gating structure to capture the global semantics. The textual relationship between the premise and the hypothesis is inferred by the three categories of attention including direct concatenation, similarity and difference. Extensive experiments were conducted to evaluate the performance of the proposed DGI model in two popular corpus by the metric of accuracy, and the results demonstrate that our approach outperforms the state-of-the-art baseline models in textual entailment in an effective manner.