Modelling Sentence Pairs with Tree-structured Attentive Encoder

By Yao Zhou, Cong Liu, Yan Pan. 2016

40 citations recorded by the source corpus.

We describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs. Since existing attentive models exert attention on the sequential structure, we propose a way to incorporate attention into the tree topology. Specially, given a pair of sentences, our attentive encoder uses the representation of one sentence, which generated via an RNN, to guide the structural encoding of the other sentence on the dependency parse tree. We evaluate the proposed attentive encoder on three tasks: semantic similarity, paraphrase identification and true-false question selection. Experimental results show that our encoder outperforms all baselines and achieves state-of-the-art results on two tasks.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Model Architectures

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Citations in the corpus

Structured Attention Networks (2017, background): “It is thought that syntactic information is useful for this task, and a state-of-the-art system uses attention over pre-constructed parse trees (Zhou et al., 2016).”