The structure of a sentence conveys rich linguistic knowledge and has proven useful for natural language understanding. In this paper, we aim to incorporate syntactical constraints and long-range word dependencies into the sentence encoding procedure using the widely applied Graph Convolutional Network (GCN) and word dependency trees. Existing syntax-aware GCN methods construct the adjacency matrix by referring to whether two words are connected in the dependency tree. But they fail to model the word dependency type, which reflects how the words are linked in dependency trees. They cannot distinguish the different contributions of different word dependency paths. To avoid introducing redundant word dependencies that harm language understanding, we propose a GCN version that is extended by a novel Word Dependency Gate mechanism. Word Dependency Gate can adaptively maintain the balance between the inclusion and exclusion of specific word dependency paths based on the word dependency type and its word context. Experiments show that our approach can effectively incorporate the relevant syntactical dependency in BERT and achieve a state-of-the-art performance in the End-to-End Aspect-Based Sentiment Analysis and Relation Triple Extraction tasks.

句子的结构传达了丰富的语言知识，并证明对自然语言理解很有用。在本文中，我们旨在使用广泛使用的图卷积网络（GCN）和单词依赖树将句法约束和远程单词依赖项纳入句子编码过程。现有的可识别语法的GCN方法通过引用在依赖树中是否连接了两个单词来构造邻接矩阵。但是他们无法对单词依赖类型进行建模，这反映了单词在依赖树中的链接方式。他们无法区分不同单词依赖路径的不同贡献。为避免引入有害于语言理解的冗余单词依赖性，我们提出了一种GCN版本，该版本由新颖的单词依赖性门机制扩展。单词相关性门可以根据单词相关性类型及其单词上下文来自适应地维护特定单词相关性路径的包含和排除之间的平衡。实验表明，我们的方法可以有效地将相关的句法依赖性纳入BERT中，并在基于端到端基于方面的情感分析和关系三重提取任务中达到最先进的性能。