In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) captures sentence-level semantics; then heuristic matching layers like concatenation, element-wise product/difference combine the information in individual sentences. Experimental results show that our model outperforms existing sentence encoding-based approaches by a large margin.
['Lili Mou', 'Rui Men', 'Ge Li', 'Yan Xu', 'Lu Zhang', 'Rui Yan', 'Zhi Jin']
Natural Language Inference by Tree-Based Convolution and Heuristic Matching