Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translation (2023)

Abstract: The vast majority of work on gender in MT focuses on ‘unambiguous’ inputs, where gender markers in the source language are expected to be resolved in the output. Conversely, this paper explores the widespread case where the source sentence lacks explicit gender markers, but the target sentence contains them due to richer grammatical gender. We particularly focus on inputs containing person names. Investigating such sentence pairs casts a new light on research into MT gender bias and its mitigation. We find that many name-gender co-occurrences in MT data are not resolvable with ‘unambiguous gender’ in the source language, and that gender-ambiguous examples can make up a large proportion of training examples. From this, we discuss potential steps toward gender-inclusive translation which accepts the ambiguity in both gender and translation.

## introduction different languages express grammatical gender to differing extents. where language refers to a person, that person's sociological gender is often expressed via grammatical gender, whether this is simply gendered pronouns in english or profession nouns in german. for machine translation, it is desirable to translate these expressions of gender to the output when they are expressed in the source, and to not express gender in the output which is not implied by the source @xcite . gender translation is well-defined when translating into a target language that marks gender in the same way as the source language, for the same parts of speech or a subset of them. difficulties arise when translating into a target language which gender-inflects more parts of speech than the source. this gender translation challenge can be split into two sub-challenges: unambiguous gender translation and ambiguous gender translation. unambiguous gender translation generally requires gender markers in the source sentence @xcite . the challenge is to resolve gendered terms in the target language consistently with the information in the source sentence. for example, translating 'she is an engineer' into german would require that 'engineer' be translated with the feminine form, 'ingenieurin', not 'ingenieur'. most research to date on gender bias and gender handling in nmt has focused on this unambiguous case @xcite . by contrast, the ambiguous case has no gender markers in the source sentence, either surface-level in the form of gendered parts-of-speech or metalevel in the form of speaker tags or user preference. for example, translating 'taylor is an engineer' into german would still involve choosing a grammatical gender for 'engineer', but it is unclear whether the best option is 'ingenieur', 'ingenieurin', or a more inclusive but less commonly used formulation such as 'ingenieur * in'. the goal here is less well-defined. one option is neutralization, avoiding terms implying gender in the output, which may be difficult depending on the output language. another option, used on a small scale in some commercial systems, is annotating and producing multiple output translations where source gender is ambiguous @xcite . the latter approach, while ui-dependent, has potential for inclusion of new gender-neutral or other nonbinary gendered terms. in this paper, we seek to motivate further research into ambiguous-gender translation. we show that ambiguous-gender translation is an underexplored task relative to its prevalence in corpora. we also perform qualitative exploration of the possibilities and challenges of ambiguous gender translation. we particularly focus on gender-ambiguous translation with reference to named entities. predicting gender from person name is unreliable and exclusionary (section 2.2). however, person names do often co-occur with terms that are gendered in a rich-morphology target language. sentences containing person names are therefore highly relevant to gender-ambiguous translation. we first describe related work on gender translation, including some flaws in common treatments of 'unambiguous' gender translation. we then describe and use a simple, high-recall method to identify parallel segments meeting our ambiguous gender criterion, where the model must infer gender despite no reliable markers in the source. we analyse the gender characteristics of the results, focusing on english translation into german, french and spanish in two domains, opensubtitles and europarl. finally, we describe some possible directions based on our findings towards genderinclusive translation technologies, with particular reference to inclusivity of those likely to be misgendered by typical name-gender proxies -anyone whose name does not conform to anglo-centric name-gender associations. ## named entities and ambiguous gender translation in this section we motivate investigation of named entities for ambiguous gender translation research, and compare named entity recognition (ner) techniques for mt training data. ## using named entities to identify target gendered language in this section we use the best-performing tc person name method from section 3 to extract lines containing likely person-names, and further analyse their characteristics in terms of gendered lan-6 grep -pwi "(she|her|hers|herself|he|him| his|himself)". we use a binary match since we find that 'they' is overwhelmingly used in the plural in the opensubtitles and europarl datasets. ## towards gender-inclusive mt in this section, we consider what steps towards gender-inclusive mt we might be able to take with access to a wider range of human-referent gendered data. we consider two broad aspects, training and evaluation. ## conclusions this paper proposes a new perspective on genderinclusive translation technologies. while most research to date has focused on resolving 'unambiguous' gender translation, we discuss the challenges of ambiguous gender translation, where a target language implies gender not grounded in the source. we show that an initial exploration of the ambiguous gender scenario related to named entities suggests possibilities for finding many examples of this scenario in parallel data. we suggest applications for this data both for gender bias mitigation and developing more gender-inclusive systems. overall, we hope to provide a practical perspective on names, gender, and the inherent ambiguities in gender-inclusive translation.