TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Natural Language Inference	SNLI	300D Tree-based CNN encoders	% Test Accuracy	82.1	# 88
Natural Language Inference	SNLI	300D Tree-based CNN encoders	% Train Accuracy	83.3	# 71
Natural Language Inference	SNLI	300D Tree-based CNN encoders	Parameters	3.5m	# 4

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Natural Language Inference by Tree-Based Convolution and Heuristic Matching

ACL 2016 · Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, Zhi Jin ·

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.