RGAT: A Deeper Look into Syntactic Dependency Information for
  Coreference Resolution
Yuan Meng Xuhao Pan Jun Chang Yue Wang
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.