  Impressive milestones have been achieved in text matching by adopting a
cross-attention mechanism to capture pertinent semantic connections between two
sentences. However, these cross-attention mechanisms focus on word-level links
between the two inputs, 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, 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 confirm the effectiveness of our model.
