Gender-Neutral Machine Translation Strategies in Practice
- URL: http://arxiv.org/abs/2506.15676v1
- Date: Wed, 18 Jun 2025 17:57:39 GMT
- Title: Gender-Neutral Machine Translation Strategies in Practice
- Authors: Hillary Dawkins, Isar Nejadgholi, Chi-kiu Lo,
- Abstract summary: Gender-inclusive machine translation (MT) should preserve gender ambiguity in the source to avoid misgendering and representational harms.<n>Here we assess the sensitivity of 21 MT systems to the need for gender neutrality in response to gender ambiguity in three translation directions of varying difficulty.
- Score: 13.511723323294339
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Gender-inclusive machine translation (MT) should preserve gender ambiguity in the source to avoid misgendering and representational harms. While gender ambiguity often occurs naturally in notional gender languages such as English, maintaining that gender neutrality in grammatical gender languages is a challenge. Here we assess the sensitivity of 21 MT systems to the need for gender neutrality in response to gender ambiguity in three translation directions of varying difficulty. The specific gender-neutral strategies that are observed in practice are categorized and discussed. Additionally, we examine the effect of binary gender stereotypes on the use of gender-neutral translation. In general, we report a disappointing absence of gender-neutral translations in response to gender ambiguity. However, we observe a small handful of MT systems that switch to gender neutral translation using specific strategies, depending on the target language.
Related papers
- Gender Bias in English-to-Greek Machine Translation [0.0]
We find persistent gender bias in translations by both Google Translate and DeepL.<n>GPT-4o shows promise, generating appropriate gendered and neutral alternatives for most ambiguous cases.
arXiv Detail & Related papers (2025-06-11T09:44:12Z) - mGeNTE: A Multilingual Resource for Gender-Neutral Language and Translation [21.461095625903504]
mGeNTE is a dataset of English-Italian/German/Spanish language pairs.<n>It enables research in both automatic Gender-Neutral Translation (GNT) and language modelling for three grammatical gender languages.
arXiv Detail & Related papers (2025-01-16T09:35:15Z) - Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
Existing machine translation gender bias evaluations are primarily focused on male and female genders.
This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words)
We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
arXiv Detail & Related papers (2024-07-23T08:13:51Z) - Probing Explicit and Implicit Gender Bias through LLM Conditional Text
Generation [64.79319733514266]
Large Language Models (LLMs) can generate biased and toxic responses.
We propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes.
arXiv Detail & Related papers (2023-11-01T05:31:46Z) - VisoGender: A dataset for benchmarking gender bias in image-text pronoun
resolution [80.57383975987676]
VisoGender is a novel dataset for benchmarking gender bias in vision-language models.
We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas.
We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
arXiv Detail & Related papers (2023-06-21T17:59:51Z) - Gender, names and other mysteries: Towards the ambiguous for
gender-inclusive translation [7.322734499960981]
This paper explores the case where the source sentence lacks explicit gender markers, but the target sentence contains them due to richer grammatical gender.
We find that many name-gender co-occurrences in MT data are not resolvable with 'unambiguous gender' in the source language.
We discuss potential steps toward gender-inclusive translation which accepts the ambiguity in both gender and translation.
arXiv Detail & Related papers (2023-06-07T16:21:59Z) - "I'm fully who I am": Towards Centering Transgender and Non-Binary
Voices to Measure Biases in Open Language Generation [69.25368160338043]
Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life.
We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation.
We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
arXiv Detail & Related papers (2023-05-17T04:21:45Z) - Gender Neutralization for an Inclusive Machine Translation: from
Theoretical Foundations to Open Challenges [11.37307883423629]
We explore gender-neutral translation (GNT) as a form of gender inclusivity and a goal to be achieved by machine translation (MT) models.
Specifically, we focus on translation from English into Italian, a language pair representative of salient gender-related linguistic transfer problems.
arXiv Detail & Related papers (2023-01-24T15:26:36Z) - They, Them, Theirs: Rewriting with Gender-Neutral English [56.14842450974887]
We perform a case study on the singular they, a common way to promote gender inclusion in English.
We show how a model can be trained to produce gender-neutral English with 1% word error rate with no human-labeled data.
arXiv Detail & Related papers (2021-02-12T21:47:48Z) - Neural Machine Translation Doesn't Translate Gender Coreference Right
Unless You Make It [18.148675498274866]
We propose schemes for incorporating explicit word-level gender inflection tags into Neural Machine Translation.
We find that simple existing approaches can over-generalize a gender-feature to multiple entities in a sentence.
We also propose an extension to assess translations of gender-neutral entities from English given a corresponding linguistic convention.
arXiv Detail & Related papers (2020-10-11T20:05:42Z) - Multi-Dimensional Gender Bias Classification [67.65551687580552]
Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text.
We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
arXiv Detail & Related papers (2020-05-01T21:23:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.