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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W19-3811.pdf
- Data
- GAP Coreference Dataset