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, 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 Loáiciga, 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 García Martínez, Pangeanic, Spain Sonja Schmer-Galunder, Smart Information Flow Technologies, US Matthias Gallé, NAVER LABS Europe, France Sverker Sikström, Lund University, Sweden Dirk Hovy, Bocconi University, Italy Carla Perez Almendros, Cardiff University, UK Jenny Björklund, Uppsala University Su Lin Blodgett, UMass Amherst Will Radford, Canvas, Australia Organizers Marta R. Costa-jussà, Universitat Politècnica 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-jussà: marta (dot) ruiz (at) upc (dot) edu
_______________________________________________ Moses-support mailing list Moses-support@mit.edu http://mailman.mit.edu/mailman/listinfo/moses-support
