What about “em”? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns
Anne Lauscher, Debora Nozza, Ehm Miltersen, Archie Crowley, Dirk Hovy
Abstract
As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals (Dev et al., 2021). In this “reality check”, we study how three commercial MT systems translate 3rd-person pronouns. Concretely, we compare the translations of gendered vs. gender-neutral pronouns from English to five other languages (Danish, Farsi, French, German, Italian), and vice versa, from Danish to English.Our error analysis shows that the presence of a gender-neutral pronoun often leads to grammatical and semantic translation errors. Similarly, gender neutrality is often not preserved. By surveying the opinions of affected native speakers from diverse languages, we provide recommendations to address the issue in future MT research.
BibTeX
@inproceedings{lauscher-etal-2023-em,
title = "What about {\textquotedblleft}em{\textquotedblright}? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns",
author = "Lauscher, Anne and
Nozza, Debora and
Miltersen, Ehm and
Crowley, Archie and
Hovy, Dirk",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.23/",
doi = "10.18653/v1/2023.acl-long.23",
pages = "377--392"
}