Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, Diana Zaiu Inkpen · 2017
The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixedlength vector with neural networks and the quality of the representation is tested with a natural language inference task.This paper describes our system (alpha) that is ranked among the top in the Shared Task, on both the in-domain test set (obtaining a 74.9% accuracy) and on the crossdomain test set (also attaining a 74.9% accuracy), demonstrating that the model generalizes well to the cross-domain data.Our model is equipped with intra-sentence gated-attention composition which helps achieve a better performance.In addition to submitting our model to the Shared Task, we have also tested it on the Stanford Natural Language Inference (SNLI) dataset.We obtain an accuracy of 85.5%, which is the best reported result on SNLI when cross-sentence attention is not allowed, the same condition enforced in RepEval 2017.