Modelling Interaction of Sentence Pair with coupled-LSTMs

EMNLP 2016  ·  Pengfei Liu, Xipeng Qiu, Xuanjing Huang ·

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. read more

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Natural Language Inference SNLI 50D stacked TC-LSTMs % Test Accuracy 85.1 # 69
% Train Accuracy 86.7 # 59
Parameters 190k # 3


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