Siamese Network cooperating with Multi-head Attention for semantic sentence matching

To compare a pair of sentences is a fundamental technology in many NLP tasks. According to the difference between the pair of sentence, we divide semantic sentence matching into two situations: Situation A is that the pair of sentences are worded with a context relationship, Situation B is that two are equal in semantics. Models for Situation A works in Situation B too, so prior deep work mostly model each sentence's representation considering the interaction of the other sentence simultaneously. However, models designed for Situation A bring redundant information for Situation B. In this paper, for sentence pairs with equivalence, we present a deep architecture with comparison-interaction separated to match two sentences, which based on Siamese network for comparison and multi-head attention for interaction information between sentence pairs. Experimental results on the latest Chinese sentence matching datasets outline the effectiveness of our approach.