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.
@article{arxiv.1512.08422,
title = {Natural Language Inference by Tree-Based Convolution and Heuristic Matching},
author = {Lili Mou and Rui Men and Ge Li and Yan Xu and Lu Zhang and Rui Yan and Zhi Jin},
journal= {arXiv preprint arXiv:1512.08422},
year = {2016}
}