  Impressive milestones have been achieved in text matching by adopting a
cross-attention mechanism to capture pertinent semantic connections between two
sentence representations. However, regular cross-attention focuses on
word-level links between the two input sequences, neglecting the importance of
contextual information. We propose a context-aware interaction network (COIN)
to properly align two sequences and infer their semantic relationship.
Specifically, each interaction block includes (1) a context-aware
cross-attention mechanism to effectively integrate contextual information when
aligning two sequences, and (2) a gate fusion layer to flexibly interpolate
aligned representations. We apply multiple stacked interaction blocks to
produce alignments at different levels and gradually refine the attention
results. Experiments on two question matching datasets and detailed analyses
demonstrate the effectiveness of our model.
