Although syntactic information is beneficial for many NLP tasks, combining it with contextual information between words to solve the coreference resolution problem needs to be further explored. In this paper, we propose an end-to-end parser that combines pre-trained BERT with a Syntactic Relation Graph Attention Network (RGAT) to take a deeper look into the role of syntactic dependency information for the coreference resolution task. In particular, the RGAT model is first proposed, then used to understand the syntactic dependency graph and learn better task-specific syntactic embeddings. An integrated architecture incorporating BERT embeddings and syntactic embeddings is constructed to generate blending representations for the downstream task. Our experiments on a public Gendered Ambiguous Pronouns (GAP) dataset show that with the supervision learning of the syntactic dependency graph and without fine-tuning the entire BERT, we increased the F1-score of the previous best model (RGCN-with-BERT) from 80.3% to 82.5%, compared to the F1-score by single BERT embeddings from 78.5% to 82.5%. Experimental results on another public dataset - OntoNotes 5.0 demonstrate that the performance of the model is also improved by incorporating syntactic dependency information learned from RGAT.

本文提出了一种将预训练的BERT与句法关系图注意力网络（RGAT）相结合的端到端解析器，用于深入研究句法依赖信息对于共指消解任务的作用。通过在公用的性别模糊代词（GAP）数据集上的实验，证明了在不微调整个BERT的情况下，我们将前一最佳模型（使用BERT的RGCN）的F1分数从80.3％提高到82.5％，相对于单个BERT嵌入的78.5％提高到82.5％。在另一个公共数据集OntoNotes 5.0上的实验结果表明，该模型通过融入RGAT中学习到的句法依赖信息也能提高性能。

RGAT: 从更深的角度研究句法依赖信息对于共指消解的作用