Gendered Ambiguous Pronoun (GAP) Shared Task at the Gender Bias in NLP Workshop 2019
- Kellie Webster,
- Marta R. Costa-jussà,
- ,
- Will Radford
- Google Inc.,
- Polytechnic University of Catalonia,
- Uppsala University,
- Canva
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review
Open access
Publication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOriginal language
EnglishPublication milestones
- Published - 02/08/2019
Publication status
Published - 02/08/2019
ISBN (Print)
978-1-950737-40-6Publication IDs
- ORCID: /0000-0002-6103-7275/work/106363249
Host publication title
Proceedings of the First Workshop on Gender Bias in Natural Language ProcessingAbstract
The 1st ACL workshop on Gender Bias in Natural Language Processing included a shared task on gendered ambiguous pronoun (GAP) resolution. This task was based on the coreference challenge defined in Webster et al. (2018), designed to benchmark the ability of systems to resolve pronouns in real-world contexts in a gender-fair way. 263 teams competed via a Kaggle competition, with the winning system achieving logloss of 0.13667 and near gender parity. We review the approaches of eleven systems with accepted description papers, noting their effective use of BERT (Devlin et al., 2018), both via fine-tuning and for feature extraction, as well as ensembling.