Multilevel dynamic gated inference network for recognizing textual entailment

Abstract:

Most existing models of recognizing textual entailment (RTE) get the interaction features between a premise and a hypothesis by an attention matrix at word level. However, the attention of important words should be different with the degree of understanding from diverse aspects, and only the local features are captured. To solve the problem above, the model with Multilevel Dynamic Gated Inference Network (MDGIN) is proposed, which combines the finegrained wordlevel information and sentencelevel gating mechanism to dynamically capture the relationships of text pairs. Moreover, the model extracts the different semantic features by diverse attention ways. In this paper, experiments are carried out on two textual datasets. Compared with the benchmark models and the existing mainstream models, the accuracy is improved by 0.4%~1.7%. The effectiveness of each part of the model is further verified by ablation analysis.