  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.
