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.
翻译:句子匹配是自然语言处理与各种应用的基本任务。 多数最近的做法都采用了基于注意的神经模型, 以在两个句子之间构建字词或字词级一致。 但是, 这些模型通常忽略句子的内在结构, 不考虑文本单位之间的各种依赖关系 。 为了解决这些问题,本文件提议了一种基于图表的句子匹配方法 。 首先, 我们代表一对句子作为图表, 配有几种仔细设计的战略 。 然后我们使用一个新的封闭式图形关注网络, 编码构建的句子匹配图 。 实验结果显示, 我们的方法在自然语言和参数识别任务之间的两个数据集上取得了最先进的性能 。 进一步的讨论显示, 我们的模式可以学习有意义的图形结构, 表明其在改进可解释性方面的优势 。