BERT Masked Language Modeling for Co-reference Resolution

Felipe Alfaro, Marta R. Costa-jussà, José A. R. Fonollosa


Abstract
This paper explains the TALP-UPC participation for the Gendered Pronoun Resolution shared-task of the 1st ACL Workshop on Gender Bias for Natural Language Processing. We have implemented two models for mask language modeling using pre-trained BERT adjusted to work for a classification problem. The proposed solutions are based on the word probabilities of the original BERT model, but using common English names to replace the original test names.
Anthology ID:
W19-3811
Volume:
Proceedings of the First Workshop on Gender Bias in Natural Language Processing
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Marta R. Costa-jussà, Christian Hardmeier, Will Radford, Kellie Webster
Venue:
GeBNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
76–81
Language:
URL:
https://aclanthology.org/W19-3811
DOI:
10.18653/v1/W19-3811
Bibkey:
Cite (ACL):
Felipe Alfaro, Marta R. Costa-jussà, and José A. R. Fonollosa. 2019. BERT Masked Language Modeling for Co-reference Resolution. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 76–81, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
BERT Masked Language Modeling for Co-reference Resolution (Alfaro et al., GeBNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-3811.pdf
Data
GAP Coreference Dataset