论文标题
用封闭的图形注意网络诱导对齐结构，以匹配句子
Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching
论文作者
论文摘要
句子匹配是与各种应用程序进行自然语言处理的基本任务。最近的方法采用基于注意力的神经模型来建立两个句子之间的单词或短语级别对齐。但是，这些模型通常会忽略句子内的固有结构，并且无法考虑文本单元之间的各种依赖关系。为了解决这些问题，本文提出了一种基于图的句子匹配方法。首先，我们将句子对表示为具有几种仔细设计策略的图表。然后，我们采用一个新颖的封闭图形注意力网络来编码构造的图表以匹配句子。实验结果表明，我们的方法基本上在自然语言和释义识别任务中实现了两个数据集上的最新性能。进一步的讨论表明，我们的模型可以学习有意义的图结构，表明其在改善的可解释性方面的优势。
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.
