@inproceedings{lardelli-etal-2024-sparks,
title = "Sparks of Fairness: Preliminary Evidence of Commercial Machine Translation as {E}nglish-to-{G}erman Gender-Fair Dictionaries",
author = "Lardelli, Manuel and
Dill, Timm and
Attanasio, Giuseppe and
Lauscher, Anne",
editor = "Savoldi, Beatrice and
Hackenbuchner, Jani{\c{c}}a and
Bentivogli, Luisa and
Daems, Joke and
Vanmassenhove, Eva and
Bastings, Jasmijn",
booktitle = "Proceedings of the 2nd International Workshop on Gender-Inclusive Translation Technologies",
month = jun,
year = "2024",
address = "Sheffield, United Kingdom",
publisher = "European Association for Machine Translation (EAMT)",
url = "https://aclanthology.org/2024.gitt-1.2",
pages = "12--21",
abstract = "Bilingual dictionaries are bedrock components for several language tasks, including translation. However, dictionaries are traditionally fixed in time, thus excluding those neologisms and neo-morphemes that challenge the language{'}s nominal morphology. The need for a more dynamic, mutable alternative makes machine translation (MT) systems become an extremely valuable avenue. This paper investigates whether commercial MT can be used as bilingual dictionaries for gender-neutral translation. We focus on the English-to-German pair, where notional gender in the source requires gender inflection in the target. We translated 115 person-referring terms using Google Translate, Microsoft Bing, and DeepL and discovered that while each system is heavily biased towards the masculine gender, DeepL often provides gender-fair alternatives to users, especially with plurals.",
}
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%0 Conference Proceedings
%T Sparks of Fairness: Preliminary Evidence of Commercial Machine Translation as English-to-German Gender-Fair Dictionaries
%A Lardelli, Manuel
%A Dill, Timm
%A Attanasio, Giuseppe
%A Lauscher, Anne
%Y Savoldi, Beatrice
%Y Hackenbuchner, Janiça
%Y Bentivogli, Luisa
%Y Daems, Joke
%Y Vanmassenhove, Eva
%Y Bastings, Jasmijn
%S Proceedings of the 2nd International Workshop on Gender-Inclusive Translation Technologies
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, United Kingdom
%F lardelli-etal-2024-sparks
%X Bilingual dictionaries are bedrock components for several language tasks, including translation. However, dictionaries are traditionally fixed in time, thus excluding those neologisms and neo-morphemes that challenge the language’s nominal morphology. The need for a more dynamic, mutable alternative makes machine translation (MT) systems become an extremely valuable avenue. This paper investigates whether commercial MT can be used as bilingual dictionaries for gender-neutral translation. We focus on the English-to-German pair, where notional gender in the source requires gender inflection in the target. We translated 115 person-referring terms using Google Translate, Microsoft Bing, and DeepL and discovered that while each system is heavily biased towards the masculine gender, DeepL often provides gender-fair alternatives to users, especially with plurals.
%U https://aclanthology.org/2024.gitt-1.2
%P 12-21
Markdown (Informal)
[Sparks of Fairness: Preliminary Evidence of Commercial Machine Translation as English-to-German Gender-Fair Dictionaries](https://aclanthology.org/2024.gitt-1.2) (Lardelli et al., GITT-WS 2024)
ACL