May 15, 2024, 4:47 a.m. | Andrea Piergentili, Beatrice Savoldi, Matteo Negri, Luisa Bentivogli

cs.CL updates on

arXiv:2405.08477v1 Announce Type: new
Abstract: Machine translation (MT) models are known to suffer from gender bias, especially when translating into languages with extensive gendered morphology. Accordingly, they still fall short in using gender-inclusive language, also representative of non-binary identities. In this paper, we look at gender-inclusive neomorphemes, neologistic elements that avoid binary gender markings as an approach towards fairer MT. In this direction, we explore prompting techniques with large language models (LLMs) to translate from English into Italian using neomorphemes. …

abstract arxiv bias binary gender gender bias language language models languages large language large language models look machine machine translation paper translation type

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