Generating Gender Alternatives in Machine Translation
This paper was accepted at the 5th Workshop on Gender Bias in Natural Language Processing 2024. Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term “the nurse”) into the gendered form that is most prevalent in the systems’ training data (e.g., “enfermera”, the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation…
Machine Translation (MT) enables people to connect with others and engage with content across language barriers. Grammatical gender presents a difficult challenge for these systems, as some languages require specificity for terms that can be ambiguous or neutral in other languages. For example, when translating the English word "nurse" into…
Multilingual Machine Translation promises to improve translation quality between non-English languages. This is advantageous for several reasons, namely lower latency (no need to translate twice), and reduced error cascades (e.g. , avoiding losing gender and formality information when translating through English). On the downside, adding more languages reduces model capacity…
We introduce ProText, a dataset for measuring gendering and misgendering in stylistically diverse long-form English texts. ProText spans three dimensions: Theme nouns (names, occupations, titles, kinship terms), Theme category (stereotypically male, stereotypically female, gender-neutral/non-gendered), and Pronoun category (masculine, feminine, gender-neutral, none). The dataset is designed to probe (mis)gendering in text…