RGAT: A Deeper Look into Syntactic Dependency Information for Coreference Resolution

1 code implementation • 10 Sep 2023 • Yuan Meng, Xuhao Pan, Jun Chang, Yue Wang

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%.

coreference-resolution Graph Attention