[Corpora-List] Deadline Extension, 28th April: ACL-IJCNLP 3rd Workshop on Gender Bias for Natural Language Processing

Marta Ruiz martaruizcostajussa at gmail.com
Tue Apr 27 10:34:57 CEST 2021


ACL-IJCNLP 3rd Workshop on Gender Bias for Natural Language Processing

http://genderbiasnlp.talp.cat

5-6 August, Bangkok, Thailand

Gender and other demographic biases (e.g. race, nationality, religion) in machine-learned models are 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. Popular approaches include building and using balanced training and evaluation datasets (e.g. Reddy & Knight, 2016, Webster et al., 2018, Maadan et al., 2018), and changing the learning algorithms themselves (e.g. Bolukbasi et al., 2016, Chiappa et al., 2018). 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. Our workshop follows up two successful previous editions of the Workshop collocated with ACL 2019 and COLING 2020, respectively. Following the successful introduction of bias statements at GeBNLP 2020, we continue to require bias statements in this year’s workshops and will again ask the program committee to engage with the bias statements in the papers they review. This helps to make clear (a) what system behaviors are considered as bias in the work, and (b) why those behaviors are harmful, in what ways, and to whom. We 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. Also, we will be keeping pushing the integration of several communities such as social sciences as well as a wider representation of approaches dealing with bias.

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 exploring demographics such as metrics to identify and assess 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.

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-IJCNLP 2021 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 that 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) <https://arxiv.org/abs/2005.14050>). More information on this requirement, which was successfully introduced at GeBNLP 2020, can be found on the workshop website <https://genderbiasnlp.talp.cat/gebnlp2020/how-to-write-a-bias-statement/>. 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.

Important dates

April 26, *April 28, 2021: Workshop Paper Due Date*

May 28, 2021: Notification of Acceptance

June 7, 2021: Camera-ready papers due

August 5-6, 2021: Workshop Dates

Keynote

Sasha Luccioni, MILA, Canada

Programme Committee

Svetlana Kiritchenko, National Council Canada, Canada

Sharid Loßiciga, University of Gothenburg, Sweden

Kaiji Lu, Carnegie Mellon University, US

Marta Recasens, Google, US

Bonnie Webber, University of Edinburgh, UK

Ben Hachey, Harrison.ai Australia

Mercedes GarcÝa MartÝnez, Pangeanic, Spain

Sonja Schmer-Galunder, Smart Information Flow Technologies, US

Matthias GallÚ, NAVER LABS Europe, France

Sverker Sikstr÷m, Lund University, Sweden

Dirk Hovy, Bocconi University, Italy

Carla Perez Almendros, Cardiff University, UK

Jenny Bj÷rklund, Uppsala University

Su Lin Blodgett, UMass Amherst

Will Radford, Canvas, Australia

Organizers

Marta R. Costa-jussÓ, Universitat PolitŔcnica de Catalunya, Barcelona

Hila Gonen, Amazon

Christian Hardmeier, IT University of Copenhagen/Uppsala University

Kellie Webster, Google AI Language, New York

Contact persons

Marta R. Costa-jussÓ: marta (dot) ruiz (at) upc (dot) edu

-- Marta Ruiz Costa-jussÓ martaruizcostajussa at gmail.com http://www.costa-jussa.com -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 22762 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20210427/c7b68932/attachment.txt>



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