  Semantic matching, which aims to determine the matching degree between two
texts, is a fundamental problem for many NLP applications. Recently, deep
learning approach has been applied to this problem and significant improvements
have been achieved. In this paper, we propose to view the generation of the
global interaction between two texts as a recursive process: i.e. the
interaction of two texts at each position is a composition of the interactions
between their prefixes as well as the word level interaction at the current
position. Based on this idea, we propose a novel deep architecture, namely
Match-SRNN, to model the recursive matching structure. Firstly, a tensor is
constructed to capture the word level interactions. Then a spatial RNN is
applied to integrate the local interactions recursively, with importance
determined by four types of gates. Finally, the matching score is calculated
based on the global interaction. We show that, after degenerated to the exact
matching scenario, Match-SRNN can approximate the dynamic programming process
of longest common subsequence. Thus, there exists a clear interpretation for
Match-SRNN. Our experiments on two semantic matching tasks showed the
effectiveness of Match-SRNN, and its ability of visualizing the learned
matching structure.
