Workshop on "Gender Bias in Natural Language Processing", August 16, Thailand ACL

First Call for papers and announcing the shared task.

http://genderbiasnlp.talp.cat

Gender bias, among other demographic biases (e.g. race, nationality, religion), in machine-learned models is of increasing interest to the scientific community and industry. Models of natural language are highly affected by such biases, which are present in widely used products and can lead to poor user experiences. There is a growing body of research into improved representations of gender in NLP models. Key example approaches are to build and use balanced training and evaluation datasets (e.g. Webster et al., 2018), and to change the learning algorithms themselves (e.g. Bolukbasi et al., 2016). While these approaches show promising results, there is more to do to solve identified and future bias issues. In order to make progress as a field, we need to create widespread awareness of bias and a consensus on how to work against it, for instance by developing standard tasks and metrics. Our workshop provides a forum to achieve this goal.

Topics of interest

We invite submissions of technical work exploring the detection, measurement, and mediation of gender bias in NLP models and applications. Other important topics are the creation of datasets, identifying and assessing relevant biases or focusing on fairness in NLP systems. Finally, the workshop is also open to non-technical work addressing sociological perspectives, and we strongly encourage critical reflections on the sources and implications of bias throughout all types of work.

In addition this year we are organising a Shared Task on Gender Bias Machine Translation evaluation (see details below)


Paper Submission Information

Submissions will be accepted as short papers (4-6 pages) and as long papers (8-10 pages), plus additional pages for references, following the ACL 2024 guidelines. Supplementary material can be added, but should not be central to the argument of the paper. Blind submission is required.

Each paper should include a statement which explicitly defines (a) what system behaviors are considered as bias in the work and (b) why those behaviors are harmful, in what ways, and to whom (cf. Blodgett et al. (2020)). More information on this requirement, which was successfully introduced at GeBNLP 2020, can be found on the workshop website. We also encourage authors to engage with definitions of bias and other relevant concepts such as prejudice, harm, discrimination from outside NLP, especially from social sciences and normative ethics, in this statement and in their work in general.


Non-archival option

The authors have the option of submitting research as non-archival, meaning that the paper will not be published in the conference proceedings. We expect these submissions to describe the same quality of work and format as archival submissions.

Important dates.

Jan 15, 2024: First call of papers

Feb 20, 2024: Second call of papers

May 10, 2024: Workshop Paper Due Date
June 5, 2024: Notification of Acceptance
June 25, 2024: Camera-ready papers due

August 16, 2024: Workshop Dates

Keynote Speakers.

Isabelle Augenstein, University of Copenhagen

Hal Daumé III, University of Maryland and Microsoft Research NYC


Organizers.

Christine Basta, Alexandria University

Marta R. Costa-jussà, FAIR, Meta,

Agnieszka Falénska, University of Stuttgart

Seraphina Goldfarb-Tarrant, Cohere

Debora Nozza, Bocconi University





Shared Task on Machine Translation Gender Bias Evaluation

Motivation

Demographic biases are relatively infrequent phenomena but present a very important problem. The development of datasets in this area has raised the interest in evaluating Natural Language Processing (NLP) models beyond standard quality terms. In Machine Translation (MT), gender bias is observed when translations show errors in linguistic gender determination despite the fact that there are sufficient gender clues in the source content for a system to infer the correct gendered forms. To illustrate this phenomenon, sentence (1) below does not contain enough linguistic clues for a translation system to decide which gendered form should be used when translating into a language where the word for doctor is gendered. Sentence (2), however, includes a gendered pronoun which most likely has the word doctor as its antecedent. Sentence (3) shows two variations of the exact sentence with the only variation of the gender inflection.

1. I didn’t feel well, so I made an appointment with my doctor.

2. My doctor is very attentive to her patients’ needs.

3. Mi amiga es una ama de casa / Mi amigo es un amo de casa. (in English, My (female/male) friend is a homemaker)

Gender bias is observed when the system produces the wrong gendered form when translating sentence (2) into a language that uses distinct gendered forms for the word doctor. A single error in the translation of an utterance the like of sentence (1) would not be sufficient to conclude that gender bias exists in the model; doing so would take consistently observing one linguistic gender over another. Finally, a lack of robustness is shown in sentence (3) if the translation quality differs in the translation of sentences in (3). It has previously been hypothesized that one possible source of gender bias is gender representation imbalance in large training and evaluation data sets, e.g. [Costa-jussà et al., 2022; Qian et al., 2022]

Goals

The goals of the shared translation task are:

To investigate the quality of MT systems on the particular case of gender preservation for tens of languages

To examine and understand special gender challenges in translating in different language families.

To investigate the performance of gender translation of low-resource, morphologically rich languages

To open to the community the first challenge of this kind

To generate up-to-date performance numbers in order to provide a basis of comparison in future research

To investigate the usefulness of multilingual and language resources

To encourage beginners and established research groups to participate and interchange discussions


Shared Task Description

We propose to evaluate the 3 cases of gender bias: gender-specific, gender robustness and unambiguous gender.



Description Task 1: Gender-specific

In the English-to-X translation direction, we evaluate the capacity of machine translation systems to generate gender-specific translations from English neutral inputs (e.g. I didn’t feel well, so I made an appointment with my doctor.) This can be illustrated by the fact that machine translation (MT) models systematically translate neutral source sentences into masculine or feminine depending on the stereotypical usage of the word (e.g. “homemakers” into “amas de casa”, which is the feminine form in Spanish and “doctors” into “médicos”, which is the masculine form in Spanish).

Description Task 2: Gender Robustness

In the X-to-English translation direction, we compare the robustness of the model when the source input only differs in gender (masculine or feminine), e.g. in Spanish: Mi amiga es una ama de casa / Mi amigo es un amo de casa.

Description Task 3: Unambiguous Gender

In the X-to-X translation direction, we evaluate the unambiguous gender translation across languages and without being English-centric, e.g, Spanish-to-Catalan: Mi amiga es una ama de casa is translated into La meva amiga és una mestressa de casa

Submission details

X Languages. In addition to English, our challenge covers 26 languages: Modern Standard Arabic, Belarusian, Bulgarian, Catalan, Czech, Danish, German, French, Italian, Lithuanian, Standard Latvian, Marathi, Dutch, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swedish, Tamil, Thai, Ukrainian, Urdu

Evaluation. The challenge will be evaluated using automatic metrics. Evaluation criteria will be in terms of overall translation quality and difference in performance for male and female sets. More details will be provided.

Submission platform. We will use the Dynabench platform for all tasks.

Important Dates.

From Jan 2024, Fill in the interest form

Mar 20, 2024: Model Submission

April 1-15, 2024: Evaluation

April 24, 2024: System paper submission deadline

May 15, 2024: Notifications of the acceptance

June 10, 2024: Camera-Ready version

August 16, 2024: Workshop at ACL

Citation

Marta Costa-jussà, Pierre Andrews, Eric Smith, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Daniel Licht, and Carleigh Wood. 2023. Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14141–14156, Singapore. Association for Computational Linguistics.