Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution

Yinchuan Xu, Junlin Yang

Abstract
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-to-end 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.
Anthology ID:
W19-3814
Volume:
Proceedings of the First Workshop on Gender Bias in Natural Language Processing
Month:
August
Year:
2019
Address:
Florence, Italy
Venue:
GeBNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
96–101
Language:
URL:
https://aclanthology.org/W19-3814
DOI:
10.18653/v1/W19-3814
Bibkey:
Cite (ACL):
Yinchuan Xu and Junlin Yang. 2019. Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 96–101, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution (Xu & Yang, GeBNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/starsem-semeval-split/W19-3814.pdf
Code
 ianycxu/RGCN-with-BERT
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