Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information

Basta, Christine; Costa-jussà, Marta R.; Fonollosa, José A. R.

doi:10.18653/v1/2020.winlp-1.25

Cited by 14 publications

(17 citation statements)

References 4 publications

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“…It facilitates the gender prediction correctly when translating from English to other gendered languages, giving control over the translation hypothesis gender. This was confirmed in recent work by [15] , who proved that adding gender helps to increase the accuracy of gendered translations. Moreover, the authors showed that increasing context has a better effect on gendered translations leading to higher performance.…”

Section: Gender Bias Within Mt Systems and Related Worksupporting

confidence: 66%

Evaluating Gender Bias in Speech Translation

Costa-jussà¹,

Basta²,

Gállego³

2020

Preprint

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The scientific community is more and more aware of the necessity to embrace pluralism and consistently represent major and minor social groups. In this direction, there is an urgent need to provide evaluation sets and protocols to measure existing biases in our automatic systems. This paper introduces WinoST, a new freely available challenge set for evaluating gender bias in speech translation. WinoST is the speech version of WinoMT which is a MT challenge set and both follow an evaluation protocol to measure gender accuracy. Using a state-of-the-art end-to-end speech translation system, we report the gender bias evaluation on 4 language pairs and we show that gender accuracy in speech translation is more than 23% lower than in MT.

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Section: Gender Bias Within Mt Systems and Related Worksupporting

confidence: 66%

Evaluating Gender Bias in Speech Translation

Costa-jussà¹,

Basta²,

Gállego³

2020

Preprint

Self Cite

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“…Adding Context. Without further information needed for training or inference, Basta et al (2020) adopt a generic approach and concatenate each sentence with its preceding one. By providing more context, they attest a slight improvement in gender translations requiring anaphoric coreference to be solved in English-Spanish.…”

Section: Model Debiasingmentioning

confidence: 99%

Gender Bias in Machine Translation

Savoldi

Gaido

Bentivogli

et al. 2021

Transactions of the Association for Computational Linguistics

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Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, studies of gender bias in MT still lack cohesion. This advocates for a unified framework to ease future research. To this end, we: i) critically review current conceptualizations of bias in light of theoretical insights from related disciplines, ii) summarize previous analyses aimed at assessing gender bias in MT, iii) discuss the mitigating strategies proposed so far, and iv) point toward potential directions for future work.

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“…Building on WinoGender and WinoBias, Stanovsky et al [2019] curate WinoMT, a probing dataset for machine translation, with sentences with stereotypical and non-stereotypical genderrole assignments. WinoMT has become widely applied as a challenge dataset for gender bias detection in MT systems [Basta et al 2020;Renduchintala et al 2021;Stafanovičs et al 2020] with developing a version of the WinoMT dataset with binary templates filled with singuar they pronoun. Similarly, the Occupations Test dataset [Escudé Font and Costa-jussà 2019] contains template sentences to test MT systems on.…”

Section: Template-based Datasetsmentioning

confidence: 99%

“…The dataset has been created for the task of correctly classifying the subject's occupation from their biography assuming that there are differences between mens' and womens' online biographies other than gender indicators De-Arteaga et al [2019]. Further, GeBioCorpus ] present a dataset with biography and gender information from Wikipedia which has been widely used to analyse gender bias in MT (for English, Spanish, and Catalan) [Basta et al 2020;Escudé Font and Costa-jussà 2019;Vanmassenhove et al 2018].…”

Section: Natural Language Based Datasetsmentioning

confidence: 99%

A Survey on Gender Bias in Natural Language Processing

Stanczak¹,

Augenstein²

2021

Preprint

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Language can be used as a means of reproducing and enforcing harmful stereotypes and biases and has been analysed as such in numerous research. In this paper, we present a survey of 304 papers on gender bias in natural language processing. We analyse definitions of gender and its categories within social sciences and connect them to formal definitions of gender bias in NLP research. We survey lexica and datasets applied in research on gender bias and then compare and contrast approaches to detecting and mitigating gender bias. We find that research on gender bias suffers from four core limitations. 1) Most research treats gender as a binary variable neglecting its fluidity and continuity. 2) Most of the work has been conducted in monolingual setups for English or other high-resource languages. 3) Despite a myriad of papers on gender bias in NLP methods, we find that most of the newly developed algorithms do not test their models for bias and disregard possible ethical considerations of their work. 4) Finally, methodologies developed in this line of research are fundamentally flawed covering very limited definitions of gender bias and lacking evaluation baselines and pipelines. We see overcoming these limitations as a necessary development in future research.

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Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information

Cited by 14 publications

References 4 publications

Evaluating Gender Bias in Speech Translation

Evaluating Gender Bias in Speech Translation

Gender Bias in Machine Translation

A Survey on Gender Bias in Natural Language Processing

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