You Shall Know a Word’s Gender by the Company it Keeps: Comparing the Role of Context in Human Gender Assumptions with MT (2024)

Abstract: In this paper, we analyse to what extent machine translation (MT) systems and humans base their gender translations and associations on role names and on stereotypicality in the absence of (generic) grammatical gender cues in language. We compare an MT system’s choice of gender for a certain word when translating from a notional gender language, English, into a grammatical gender language, German, with thegender associations of humans. We outline a comparative case study of gender translation and annotation of words in isolation, out-of-context, and words in sentence contexts. The analysis reveals patterns of gender (bias) by MT and gender associations by humans for certain (1) out-of-context words and (2) words in-context. Our findings reveal the impact of context on gender choice and translation and show that word-level analyses fall short in such studies.

## introduction aligned with a growing interest and use of language technologies as well as a demand for gender inclusiveness in society, gender bias in machine translation (mt) systems and large language models (llms) is an increasingly studied phenomenon with varying research approaches. due to the nature of how mt systems, and natural language processing (nlp) systems in general, are trained based on large language corpora, these systems exhibit and exacerbate biases present in these corpora @xcite . with biases being an inherently useful characteristic for machine learning systems to generalise on unseen data @xcite , they can lead to unfair and harmful stereotypes, such as when referring to a person using an inaccurate gender @xcite . previous research on potential triggers of gender bias in mt is often limited to word-level analyses and does not take context into account. the study presented in this paper is part of a broader research project that aims to fill gaps in current studies by focusing on the influence of sentence context on gender translations @xcite . mt systems primarily translate into generic masculine @xcite , however, we hypothesise that context can be a deciding factor for mt systems, as well as for humans, to change the gender inflection in their output. to raise awareness of why this might be happening or of where mt should be adapting gender, the goal of a broader research project, of which this study is a part of, is the creation of a detection system that analyses english source data to detect and mark words and phrases that are considered to influence the gender inflection in target translations. in comparison to what mt systems do, it is important to understand how humans perceive gender of words in isolation, out-of-context, and how those perceptions change for words in context. humans would be well aided to have additional support when machine translating text to ascertain correct and fair gender translations. the study presented in this paper compares gender bias in mt systems with inherent gender associations perceived by humans. we comparatively analysed (1) how an mt system translates a person's gender of a word out-of-context (i.e. in iso-lation) versus in a sentence context, (2) the individual differences between human annotators of gender associations of words out-of-context and incontext, and (3) the comparison of mt with human associations with a focus on gender. in the following sections, we cover related research (section 2), how the data was collected (section 3), the process of participatory data annotation (section 4), data analysis of both human annotators and mt outputs (section 5), limitations (section 7) and a conclusion (section 6). ## related research research shows that humans are strongly influenced by how gender is expressed in languages, by role names and by general stereotypes @xcite @xcite . humans construct their own individual representations of gender, which, if available, they base on grammar in language (e.g., waitress) but when lacking grammatical cues, they base on stereotype information @xcite . in grammatical gender languages, such as german @xcite , people are often referred to in the generic masculine which is intended to be generic but is not typically interpreted as such @xcite . stereotypicality and bias further come into play when language has no grammatical gender cues, lacks pronouns or other gender referents, and the gender interpretation is up to the reader to define or an mt system to translate. based on previous research, we analysed to what extent, in the absence of grammatical gender cues, mt systems and humans base their gender translations and associations on role names and on stereotypicality. previous studies on gender in monolingual english data focused on gender inherently manifested in word embeddings by measuring the genderinflection on a word level @xcite . this has not yet been applied to sentence level nor in the context of mt. previous research on gender in mt includes the creation of challenge sets to test gender bias in mt outputs, for instance based on professions and adjectives, to balance out the gender of these pre-determined words in machine translations @xcite @xcite . such challenge sets follow the format of, for example, containing a female and a male sentence for "the choreogra-pher finished her work. / the choreographer finished his work." to fine-tune and therefore balance an mt system on both gendered versions (saunders and byrne, 2020). moreover, existing work on gender bias in mt has predominantly focused on translations of the binary gender, namely male and female, not taking into account the non-binary community, with the exception of few approaches taken, as by @xcite , @xcite and @xcite . a recent study on the comparison of human and model evaluations of gender bias concluded that, under constrained settings, "model biases reflect human decision-making" and that humans make (sometimes wrong) predictions based on societal and cognitive presupposition @xcite . in the study presented here, we analyse to what extent mt gender translations (model choices) coincide with human associations of gender. starting with words taken out-of-context whose word embeddings have an inherent genderinflection as well as a list of sentences featuring these words in varying contexts, this research focuses on how differently or similarly humans and mt associate gender with certain words on an individual out-of-context level and how this genderinflection is affected by sentence context. in this way, we expand on previous research but broaden the scope by collecting natural contexts that influence gender-inflections in translations rather than artificially constructed test sentences, and by extending the gender categories to include nonbinary. ## data description the data used for this study is in english, a notional gender language (mcconnell-ginet, 2013), where role names generally do not have a gender assigned, e.g., poet, apart from kinship relations (mother, father) or a few exceptions (actor, actress). english data was filtered from monolingual english corpora (statmt's news-crawl foot_0 , as well as c4 @xcite and wiki @xcite as made available on huggingface foot_1 ). the mt output is analysed in german, a grammatical gender language (mcconnell-ginet, 2013), where gender is specified. compiled list of individual words coordinator flight attendant musician opponent socialite therapist °lover °mechanic dancer °visitor colleague companion author °clerk °student accountant designer °baker writer °consumer poet bookkeeper counselor friend guard officer °user supporter judge fighter dealer soldier player manager contractor captain farmer maestro boss driver idiot cook filmmaker admirer follower salesperson buddy winner °construction worker ## analysis in this paper, we focus on a quantitative analysis of selected aspects of the annotations we obtained. we comparatively analysed (1) how an mt system translates a person's gender out-of-context versus in a sentence context, (2) the individual differences between human annotators of gender associations of words out-of-context and in-context, and (3) the comparison of mt with human associations with a focus on gender. ## conclusion in this paper, we analysed to what extent an mt system translates and humans associate gen-der with role names both out-of-context and incontext, with no grammatical gender cues in (the source) language. we note that the mt system is very rigid in its gender translations of role names, primarily translating into generic masculine, particularly for words out-of-context, and seldom changing a role name's gender in certain sentence contexts. human associations of gender are much more varied, both for words out-of-context and in-context. we particularly see that all annotators have been greatly influenced by the sentence contexts, annotating role names with a gender they associated with that specific context. the results from this study show that, in comparison to mt, humans have a much more varied understanding of gender and are highly influenced by context. this underlines the diversity and complexity of human associations and gender roles in society and therefore critically highlights the problematic generic masculine translation outputs by mt systems. the study conducted shows the necessity for continued research to further understand what these diverse human associations for gender in context mean for mt translations. on the one hand, we criticise the generic masculine output of mt systems but on the other hand we see that, for some sentences, the mt does change the gender of role names based on context. this pattern of when and why mt changes gender based on context, and to what extent this relates to human gender associations, will be further studied in the broader research project.