Mind the Inclusivity Gap: Multilingual Gender-Neutral Translation Evaluation with mGeNTE

• URL: http://arxiv.org/abs/2501.09409v3
• Date: Thu, 18 Sep 2025 17:48:23 GMT
• Title: Mind the Inclusivity Gap: Multilingual Gender-Neutral Translation Evaluation with mGeNTE
• Authors: Beatrice Savoldi, Giuseppe Attanasio, Eleonora Cupin, Eleni Gkovedarou, Janiça Hackenbuchner, Anne Lauscher, Matteo Negri, Andrea Piergentili, Manjinder Thind, Luisa Bentivogli,
• Abstract summary: Gender-neutral translation (GNT) represents a linguistic strategy towards fairer communication across languages.<n>We introduce mGeNTE, an expert-curated resource, and use it to conduct the first systematic multilingual evaluation of inclusive translation.<n>Experiments on en-es/de/it/el reveal that while models can recognize when neutrality is appropriate, they cannot consistently produce neutral translations.
• Score: 34.11872938329087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Avoiding the propagation of undue (binary) gender inferences and default masculine language remains a key challenge towards inclusive multilingual technologies, particularly when translating into languages with extensive gendered morphology. Gender-neutral translation (GNT) represents a linguistic strategy towards fairer communication across languages. However, research on GNT is limited to a few resources and language pairs. To address this gap, we introduce mGeNTE, an expert-curated resource, and use it to conduct the first systematic multilingual evaluation of inclusive translation with state-of-the-art instruction-following language models (LMs). Experiments on en-es/de/it/el reveal that while models can recognize when neutrality is appropriate, they cannot consistently produce neutral translations, limiting their usability. To probe this behavior, we enrich our evaluation with interpretability analyses that identify task-relevant features and offer initial insights into the internal dynamics of LM-based GNT.

Related papers

• Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language [21.87606488958834]
  We present five German datasets for gender bias evaluation in large language models (LLMs)<n>The datasets are grounded in well-established concepts of gender bias and are accessible through multiple methodologies.<n>Our findings, reported for eight multilingual LLM models, reveal unique challenges associated with gender bias in German.
  arXiv  Detail & Related papers  (2025-07-22T13:09:41Z)
• EuroGEST: Investigating gender stereotypes in multilingual language models [53.88459905621724]
  Large language models increasingly support multiple languages, yet most benchmarks for gender bias remain English-centric.<n>We introduce EuroGEST, a dataset designed to measure gender-stereotypical reasoning in LLMs across English and 29 European languages.
  arXiv  Detail & Related papers  (2025-06-04T11:58:18Z)
• Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models [56.61984030508691]
  We present the first mechanistic interpretability study of language confusion.<n>We show that confusion points (CPs) are central to this phenomenon.<n>We show that editing a small set of critical neurons, identified via comparative analysis with a multilingual-tuned counterpart, substantially mitigates confusion.
  arXiv  Detail & Related papers  (2025-05-22T11:29:17Z)
• Breaking Language Barriers or Reinforcing Bias? A Study of Gender and Racial Disparities in Multilingual Contrastive Vision Language Models [14.632649933582648]
  We present the first systematic audit of three public multilingual CLIP checkpoints -- M-CLIP, NLLB-CLIP, and CAPIVARA-CLIP.<n>We quantify race and gender bias and measure stereotype amplification.<n> Highly gendered languages consistently magnify all measured bias types, but even gender-neutral languages remain vulnerable when cross-lingual weight sharing imports foreign stereotypes.
  arXiv  Detail & Related papers  (2025-05-20T10:14:00Z)
• An LLM-as-a-judge Approach for Scalable Gender-Neutral Translation Evaluation [14.799630514233238]
  Gender-neutral translation (GNT) aims to avoid expressing the gender of human referents when the source text lacks explicit cues about the gender of those referents.<n>We investigate the use of large language models (LLMs) as evaluators of GNT.
  arXiv  Detail & Related papers  (2025-04-16T10:14:27Z)
• The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification [57.06913662622832]
  Gender-fair language fosters inclusion by addressing all genders or using neutral forms. Gender-fair language substantially impacts predictions by flipping labels, reducing certainty, and altering attention patterns. While we offer initial insights on the effect on German text classification, the findings likely apply to other languages.
  arXiv  Detail & Related papers  (2024-09-26T15:08:17Z)
• 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)
• What an Elegant Bridge: Multilingual LLMs are Biased Similarly in Different Languages [51.0349882045866]
  This paper investigates biases of Large Language Models (LLMs) through the lens of grammatical gender. We prompt a model to describe nouns with adjectives in various languages, focusing specifically on languages with grammatical gender. We find that a simple classifier can not only predict noun gender above chance but also exhibit cross-language transferability.
  arXiv  Detail & Related papers  (2024-07-12T22:10:16Z)
• Leveraging Large Language Models to Measure Gender Representation Bias in Gendered Language Corpora [9.959039325564744]
  Gender bias in text corpora can lead to perpetuation and amplification of societal inequalities. Existing methods to measure gender representation bias in text corpora have mainly been proposed for English. This paper introduces a novel methodology to quantitatively measure gender representation bias in Spanish corpora.
  arXiv  Detail & Related papers  (2024-06-19T16:30:58Z)
• Gender Bias in Large Language Models across Multiple Languages [10.068466432117113]
  We examine gender bias in large language models (LLMs) generated for different languages. We use three measurements: 1) gender bias in selecting descriptive words given the gender-related context. 2) gender bias in selecting gender-related pronouns (she/he) given the descriptive words.
  arXiv  Detail & Related papers  (2024-03-01T04:47:16Z)
• On Evaluating and Mitigating Gender Biases in Multilingual Settings [5.248564173595024]
  We investigate some of the challenges with evaluating and mitigating biases in multilingual settings. We first create a benchmark for evaluating gender biases in pre-trained masked language models. We extend various debiasing methods to work beyond English and evaluate their effectiveness for SOTA massively multilingual models.
  arXiv  Detail & Related papers  (2023-07-04T06:23:04Z)
• Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation [12.376309678270275]
  bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT. We study the effect of encouraging language-agnostic hidden representations on models' ability to preserve gender. We find that language-agnostic representations mitigate zero-shot models' masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement.
  arXiv  Detail & Related papers  (2023-05-26T13:51:50Z)
• "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)
• Analyzing Gender Representation in Multilingual Models [59.21915055702203]
  We focus on the representation of gender distinctions as a practical case study. We examine the extent to which the gender concept is encoded in shared subspaces across different languages.
  arXiv  Detail & Related papers  (2022-04-20T00:13:01Z)
• Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation [20.39599469927542]
  Gender bias is largely recognized as a problematic phenomenon affecting language technologies. Most of current evaluation practices adopt a word-level focus on a narrow set of occupational nouns under synthetic conditions. Such protocols overlook key features of grammatical gender languages, which are characterized by morphosyntactic chains of gender agreement.
  arXiv  Detail & Related papers  (2022-03-18T11:14:16Z)
• 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)
• Decoding and Diversity in Machine Translation [90.33636694717954]
  We characterize differences between cost diversity paid for the BLEU scores enjoyed by NMT. Our study implicates search as a salient source of known bias when translating gender pronouns.
  arXiv  Detail & Related papers  (2020-11-26T21:09:38Z)
• 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)
• Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias [5.239305978984572]
  We show that for languages with type B reflexivization, we can construct multi-task challenge datasets for detecting gender bias. In these languages, the direct translation of 'the doctor removed his mask' is not ambiguous between a coreferential reading and a disjoint reading. We present a multilingual, multi-task challenge dataset, which spans four languages and four NLP tasks.
  arXiv  Detail & Related papers  (2020-09-24T23:47:18Z)
• Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer [101.58431011820755]
  We study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations.
  arXiv  Detail & Related papers  (2020-05-02T04:34:37Z)

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.