Abstract
Recent works have found evidence of gender bias in models of machine translation and coreference resolution using mostly synthetic diagnostic datasets. While these quantify bias in a controlled experiment, they often do so on a small scale and consist mostly of artificial, out-of-distribution sentences. In this work, we find grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments (e.g., female nurses versus male dancers) in corpora from three domains, resulting in a first large-scale gender bias dataset of 108K diverse real-world English sentences. We manually verify the quality of our corpus and use it to evaluate gender bias in various coreference resolution and machine translation models. We find that all tested models tend to over-rely on gender stereotypes when presented with natural inputs, which may be especially harmful when deployed in commercial systems. Finally, we show that our dataset lends itself to finetuning a coreference resolution model, finding it mitigates bias on a held out set. Our dataset and models are publicly available at github.com/SLAB-NLP/BUG. We hope they will spur future research into gender bias evaluation mitigation techniques in realistic settings.- Anthology ID:
- 2021.findings-emnlp.211
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2470–2480
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.211
- DOI:
- 10.18653/v1/2021.findings-emnlp.211
- Cite (ACL):
- Shahar Levy, Koren Lazar, and Gabriel Stanovsky. 2021. Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2470–2480, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (Levy et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2021.findings-emnlp.211.pdf
- Code
- slab-nlp/bug
- Data
- BUG, GAP Coreference Dataset, OPUS-MT, WinoBias