Proceedings of the First Workshop on Gender-Inclusive Translation Technologies

Eva Vanmassenhove, Beatrice Savoldi, Luisa Bentivogli, Joke Daems, Janiça Hackenbuchner (Editors)

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Tampere, Finland
European Association for Machine Translation
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Proceedings of the First Workshop on Gender-Inclusive Translation Technologies
Eva Vanmassenhove | Beatrice Savoldi | Luisa Bentivogli | Joke Daems | Janiça Hackenbuchner

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The User-Aware Arabic Gender Rewriter
Bashar Alhafni | Ossama Obeid | Nizar Habash

We introduce the User-Aware Arabic Gender Rewriter, a user-centric web-based system for Arabic gender rewriting in contexts involving two users. The system takes either Arabic or English sentences as input, and provides users with the ability to specify their desired first and/or second person target genders. The system outputs gender rewritten alternatives of the Arabic sentences (provided directly or as translation outputs) to match the target users’ gender preferences.

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Gender-Fair Language in Translation: A Case Study
Angela Balducci Paolucci | Manuel Lardelli | Dagmar Gromann

With an increasing visibility of non-binary individuals, a growing number of language-specific strategies to linguistically include all genders or neutralize any gender references can be observed. Due to this multiplicity of proposed strategies and gender-specific grammatical differences across languages, selecting the one option to translate gender-fair language is challenging for machines and humans alike. As a first step towards gender-fair translation, we conducted a survey with translators to compare four gender-fair translations from a notional gender language, English, to a grammatical gender language, German. Proposed translations were rated by means of best-worst scaling as well as regarding their readability and comprehensibility. Participants expressed a clear preference for strategies with gender-inclusive character, i.e., colon.

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Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation
Lena Cabrera | Jan Niehues

Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation between grammatical gender languages which requires preserving the inherent gender information from the source in the target language. We study the effect of encouraging language-agnostic hidden representations on models’ ability to preserve gender and compare pivot-based and zero-shot translation regarding the influence of the bridge language (participating in all language pairs during training) on gender preservation. 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.

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Gender-inclusive translation for a gender-inclusive sport: strategies and translator perceptions at the International Quadball Association
Joke Daems

Gender-inclusive language is of key importance to the IQA, the international governing body for quadball, a mixed-gender contact sport that explicitly welcomes players of all genders. While relatively straightforward for English, the picture becomes more complicated for most of the other IQA working languages. This paper provides an overview of the strategies currently chosen by translation team leaders for different IQA languages, the factors that influenced this decision and their connection with existing research on inclusive language strategies. It further explores the awareness and attitudes of IQA translators towards those strategies and factors.

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Participatory Research as a Path to Community-Informed, Gender-Fair Machine Translation
Dagmar Gromann | Manuel Lardelli | Katta Spiel | Sabrina Burtscher | Lukas Daniel Klausner | Arthur Mettinger | Igor Miladinovic | Sigrid Schefer-Wenzl | Daniela Duh | Katharina Bühn

Recent years have seen a strongly increased visibility of non-binary people in public discourse. Accordingly, considerations of gender-fair language go beyond a binary conception of male/female. However, language technology, especially machine translation (MT), still suffers from binary gender bias. Proposing a solution for gender-fair MT beyond the binary from a purely technological perspective might fall short to accommodate different target user groups and in the worst case might lead to misgendering. To address this challenge, we propose a method and case study building on participatory action research to include experiential experts, i.e., queer and non-binary people, translators, and MT experts, in the MT design process. The case study focuses on German, where central findings are the importance of context dependency to avoid identity invalidation and a desire for customizable MT solutions.

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Reducing Gender Bias in NMT with FUDGE
Tianshuai Lu | Noëmi Aepli | Annette Rios

Gender bias appears in many neural machine translation (NMT) models and commercial translation software. Research has become more aware of this problem in recent years and there has been work on mitigating gender bias. However, the challenge of addressing gender bias in NMT persists. This work utilizes a controlled text generation method, Future Discriminators for Generation (FUDGE), to reduce the so-called Speaking As gender bias. This bias emerges when translating from English to a language that openly marks the gender of the speaker. We evaluate the model on MuST-SHE, a challenge set to specifically evaluate gender translation. The results demonstrate improvements in the translation accuracy of the feminine terms.

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Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges
Andrea Piergentili | Dennis Fucci | Beatrice Savoldi | Luisa Bentivogli | Matteo Negri

Gender inclusivity in language technologies has become a prominent research topic. In this study, we explore gender-neutral translation (GNT) as a form of gender inclusivity and a goal to be achieved by machine translation (MT) models, which have been found to perpetuate gender bias and discrimination. Specifically, we focus on translation from English into Italian, a language pair representative of salient gender-related linguistic transfer problems. To define GNT, we review a selection of relevant institutional guidelines for gender-inclusive language, discuss its scenarios of use, and examine the technical challenges of performing GNT in MT, concluding with a discussion of potential solutions to encourage advancements toward greater inclusivity in MT.

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Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translation
Danielle Saunders | Katrina Olsen

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.

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How adaptive is adaptive machine translation, really? A gender-neutral language use case
Aida Kostikova | Joke Daems | Todor Lazarov

This study examines the effectiveness of adaptive machine translation (AMT) for gender-neutral language (GNL) use in English-German translation using the ModernMT engine. It investigates gender bias in initial output and adaptability to two distinct GNL strategies, as well as the influence of translation memory (TM) use on adaptivity. Findings indicate that despite inherent gender bias, machine translation (MT) systems show potential for adapting to GNL with appropriate exposure and training, highlighting the importance of customisation, exposure to diverse examples, and better representation of different forms for enhancing gender-fair translation strategies.