Natural Language Inference by Tree-Based Convolution and Heuristic Matching

Understand

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.