Modelling interaction of sentence pair with coupled-LSTMs

23Citations
Citations of this article
146Readers
Mendeley users who have this article in their library.

Abstract

Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks. However, most of the existing methods encode two sequences with separate encoders, in which a sentence is encoded with little or no information from the other sentence. In this paper, we propose a deep architecture to model the strong interaction of sentence pair with two coupled-LSTMs. Specifically, we introduce two coupled ways to model the interdependences of two LSTMs, coupling the local contextualized interactions of two sentences. We then aggregate these interactions and use a dynamic pooling to select the most informative features. Experiments on two very large datasets demonstrate the efficacy of our proposed architectures.

Cite

CITATION STYLE

APA

Liu, P., Qiu, X., Zhou, Y., Chen, J., & Huang, X. (2016). Modelling interaction of sentence pair with coupled-LSTMs. In EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 1703–1712). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d16-1176

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free