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.

翻译：在文本匹配方面已经取得了令人印象深刻的里程碑,采用了一种交叉注意机制,以捕捉两个句子之间的相关语义联系;然而,这些交叉注意机制侧重于两个投入之间的字级联系,忽视了背景信息的重要性。我们提议了一个有背景意识的互动网络(COIN),以适当匹配两个序列并推断其语义关系。具体地说,每个互动区块包括:(1)一个有背景意识的交叉注意机制,以有效地整合背景信息;(2)一个门形组合层,以灵活地相互调和表达。我们采用多个堆叠式互动块,在不同级别形成一致,并逐步改善关注结果。关于两个匹配数据集和详细分析的实验证实了我们模式的有效性。