Connectionists: 1st CFP COLING 2nd Workshop on Gender Bias for Natural Language Processing

[Marta Ruiz]

1st CFP COLING 2nd Workshop on Gender Bias for Natural Language Processing

http://genderbiasnlp.talp.cat

14th September, Barcelona





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 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. To make progress
as a field, we need standard tasks that quantify bias.



Shared Task

We will be hosting a shared task on Kaggle in the lead up to the workshop.
The task will focus on gender bias in the resolution of reflexive
possessive pronouns across languages. Competing teams will be asked to
submit systems that perform a range of core tests of natural language
understanding (coreference resolution, natural language inference, language
modeling) on examples featuring ambiguous reflexive possessive pronouns in
three languages, and measured on the level of bias they exhibit across
tasks.

Instructions to participants will be published on the workshop website. To
encourage inclusion, Kaggle will offer to compute resources to all
competing teams.



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 sociological 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 COLING
2020 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). There is no need to anonymize papers in this shared task
submission.





Important dates

Shared Task

March 2. Competition open (development data available)

April 20-24. Official evaluation (test data available)

May 1. Winners announced

May 20. Shared task papers due

Technical Papers

May 20. Deadline for Submission

June 24. Notification of acceptance

July 11. Camera ready submission



Keynote

Natalie Schluter, University of Copenhagen



Programme Committee



Svetlana Kiritchenko, National Council Canada, Canada

Kai-Wei Chang, University of Washington, US

Sharid Loáiciga, University of Gothenburg, Sweden

Zhengxian Gong, Soochow University, China

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

Michal Kosinski, Stanford University, US



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
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