Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling

Linqing Liu, Wei Yang, Jinfeng Rao, Raphael Tang, Jimmy Lin


Abstract
Semantic similarity modeling is central to many NLP problems such as natural language inference and question answering. Syntactic structures interact closely with semantics in learning compositional representations and alleviating long-range dependency issues. How-ever, such structure priors have not been well exploited in previous work for semantic mod-eling. To examine their effectiveness, we start with the Pairwise Word Interaction Model, one of the best models according to a recent reproducibility study, then introduce components for modeling context and structure using multi-layer BiLSTMs and TreeLSTMs. In addition, we introduce residual connections to the deep convolutional neural network component of the model. Extensive evaluations on eight benchmark datasets show that incorporating structural information contributes to consistent improvements over strong baselines.
Anthology ID:
D19-1114
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1204–1209
Language:
URL:
https://www.aclweb.org/anthology/D19-1114
DOI:
10.18653/v1/D19-1114
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-1114.pdf