1-2 Aug 2019
Florence, Italy
1st ACL Workshop on Gender Bias for Natural Language Processing http://genderbiasnlp.talp.cat 1st/2nd August, Florence Gender and other demographic biases in machine-learned models are of increasing interest to the scientific community and industry. Models of natural language are highly affected by such perceived biases, present in widely used products, can lead to poor user experiences. There is a growing body of research into fair representations of gender in NLP models. Key example approaches are to build and use fairer training and evaluation datasets (e.g. Reddy & Knight, 2016, Webster et al., 2018, Maadan et al., 2018), and to change 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 standard tasks which quantify bias. This workshop will be the first dedicated to the issue of gender bias in NLP techniques and it includes a shared task on coreference resolution. In order to make progress as a field, this workshop will specially focus on discussing and proposing standard tasks which quantify bias. Shared Task We invite work on gender-fair modeling via our shared task, coreference resolution on GAP (Webster et al. 2018). GAP is a coreference dataset designed to highlight current challenges for the resolution of ambiguous pronouns in context. GAP is a gender-balanced dataset and evaluation is gender disaggregated. Previous work has shown state-of-the-art resolvers are biased to yield better performance on masculine pronouns due to differences in the public discourse between genders. Participation will be via Kaggle, with submissions open over a three month period in the lead up to the workshop. 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 welcoming socialogical perspectives. 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 2019 guidelines. Supplementary material can be added. Blind submission is required. Shared task participants will be invited to submit short papers (4-6 pages, plus references). No need to anonymize papers in this shared task submission. Important dates Shared Task Jan 21. Baseline system released April 15-21. Test phase April 26. Results announced May 3. Submission of system description papers May 17. Description paper reviews completed May 30. Camera-ready description papers due Technical Papers April 26. Deadline for Submission May 15. Notification of acceptance May 22. Camera ready submission Keynote Speaker Pascale Fung, Hong Kong University of Science and Technology Programme Committee Cristina España-Bonet, DFKI, Germany Silvia Chiappa, DeepMind, UK Rachel Rudinger, John Hopkins University, US Saif Mohammad, National Council Canada Svetlana Kiritchenko, National Council Canada Corina Koolen, University of Amsterdam Kai-Wei Chang, University of Washington Kaiji Lu, Carnegie Mellon University, US Sameep Mehta, IBM Research India Sharid Loáiciga, University of Gothenburg Zhengxian Gong, Soochow University Marta Recasens, Google, US Jason Baldridge, Google AI Language, US Bonnie Webber, University of Edinburgh Ben Hachey, The University of Sydney, Australia Organizers Marta R. Costa-jussà, Universitat Politècnica de Catalunya, Barcelona Christian Hardmeier, Uppsala University Kellie Webster, Google AI Language, New York Will Radford, Canva, Sydney Contact persons General Workshop: Marta R. Costa-jussà: marta (dot) ruiz (at) upc (dot) edu Shared Task: Kellie Webster: websterk (at) google (dot) com -- [LOGIC] mailing list http://www.dvmlg.de/mailingliste.html Archive: http://www.illc.uva.nl/LogicList/ provided by a collaboration of the DVMLG, the Maths Departments in Bonn and Hamburg, and the ILLC at the Universiteit van Amsterdam