Modelling Interaction Of Sentence Pair With Coupled-lstms
Pengfei Liu, Xipeng Qiu, Xuanjing Huang . Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing 2016 – 43 citations
[Paper]
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 architecture and its superiority to state-of-the-art methods.