MiTTenS: A Dataset for Evaluating Gender Mistranslation
Kevin Robinson, Sneha Kudugunta, Romina Stella, Sunipa Dev, Jasmijn Bastings
Abstract
Translation systems, including foundation models capable of translation, can produce errors that result in gender mistranslation, and such errors can be especially harmful. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages from a variety of language families and scripts, including several traditionally under-represented in digital resources. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both neural machine translation systems and foundation models, and show that all systems exhibit gender mistranslation and potential harm, even in high resource languages.
BibTeX
@inproceedings{robinson-etal-2024-mittens,
title = "{M}i{TT}en{S}: A Dataset for Evaluating Gender Mistranslation",
author = "Robinson, Kevin and
Kudugunta, Sneha and
Stella, Romina and
Dev, Sunipa and
Bastings, Jasmijn",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.238/",
doi = "10.18653/v1/2024.emnlp-main.238",
pages = "4115--4124"
}