  Sentence matching is a fundamental task of natural language processing with
various applications. Most recent approaches adopt attention-based neural
models to build word- or phrase-level alignment between two sentences. However,
these models usually ignore the inherent structure within the sentences and
fail to consider various dependency relationships among text units. To address
these issues, this paper proposes a graph-based approach for sentence matching.
First, we represent a sentence pair as a graph with several carefully design
strategies. We then employ a novel gated graph attention network to encode the
constructed graph for sentence matching. Experimental results demonstrate that
our method substantially achieves state-of-the-art performance on two datasets
across tasks of natural language and paraphrase identification. Further
discussions show that our model can learn meaningful graph structure,
indicating its superiority on improved interpretability.
