Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution
Yinchuan Xu, Junlin Yang · 2019
Gender bias has been found in existing coreference resolvers.In order to eliminate gender bias, a gender-balanced dataset Gendered Ambiguous Pronouns (GAP) has been released and the best baseline model achieves only 66.9% F1.Bidirectional Encoder Representations from Transformers (BERT) has broken several NLP task records and can be used on GAP dataset.However, fine-tune BERT on a specific task is computationally expensive.In this paper, we propose an end-toend resolver by combining pre-trained BERT with Relational Graph Convolutional Network (R-GCN).R-GCN is used for digesting structural syntactic information and learning better task-specific embeddings.Empirical results demonstrate that, under explicit syntactic supervision and without the need to fine tune BERT, R-GCN's embeddings outperform the original BERT embeddings on the coreference task.Our work significantly improves the snippet-context baseline F1 score on GAP dataset from 66.9% to 80.3%.We participated in the Gender Bias for Natural Language Processing 2019 shared task, and our codes are available online. 1 * * Equal contribution. 1 Our codes and models are available at: https:// github.com/ianycxu/RGCN-with-BERT.