Modelling Interaction of Sentence Pair with Coupled-LSTMs
Pengfei Liu, Xipeng Qiu, Yaqian Zhou, Jifan Chen, Xuanjing Huang · 2016
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.