Natural Language Inference by Tree-Based Convolution and Heuristic Matching
Full Text: P16-2022.pdf
In this paper, we propose the TBCNNpair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) captures sentencelevel 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.
Citation
L. Mou, R. Men, G. Li, Y. Xu, L. Zhang, R. Yan, Z. Jin. "Natural Language Inference by Tree-Based Convolution and Heuristic Matching". International Conference on Computational Linguistics and the Association for Computational Linguist, pp 130–136, August 2016.| Keywords: | |
| Category: | In Conference |
| Web Links: | doi |
| ACL |
BibTeX
@incollection{Mou+al:ACL16,
author = {Lili Mou and Rui Men and Ge Li and Yan Xu and Lu Zhang and Rui Yan
and Zhi Jin},
title = {Natural Language Inference by Tree-Based Convolution and Heuristic
Matching},
Pages = {130–136},
booktitle = {International Conference on Computational Linguistics and the
Association for Computational Linguist},
year = 2016,
}Last Updated: February 04, 2021Submitted by Sabina P
