Abstract
We ask the question: Are there widespread disparities in machine translations of names across race/ethnicity, and gender? We hypothesize that the translation quality of names and surrounding context will be lower for names associated with US racial and ethnic minorities due to these systems’ tendencies to standardize language to predominant language patterns. We develop a dataset of names that are strongly demographically aligned and propose a translation evaluation procedure based on round-trip translation. We analyze the effect of name demographics on translation quality using generalized linear mixed effects models and find that the ability of translation systems to correctly translate female-associated names is significantly lower than male-associated names. This effect is particularly pronounced for female-associated names that are also associated with racial (Black) and ethnic (Hispanic) minorities. This disparity in translation quality between social groups for something as personal as someone’s name has significant implications for people’s professional, personal, and cultural identities, self-worth and ease of communication. Our findings suggest that more MT research is needed to improve the translation of names and to provide high-quality service for users regardless of gender, race, and ethnicity.- Anthology ID:
- 2023.emnlp-main.239
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3933–3945
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.239
- DOI:
- 10.18653/v1/2023.emnlp-main.239
- Cite (ACL):
- Sandra Sandoval, Jieyu Zhao, Marine Carpuat, and Hal Daumé III. 2023. A Rose by Any Other Name would not Smell as Sweet: Social Bias in Names Mistranslation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3933–3945, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- A Rose by Any Other Name would not Smell as Sweet: Social Bias in Names Mistranslation (Sandoval et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2023.emnlp-main.239.pdf