Connectionists: 1st CFP NAACL 2022 4th Workshop on Gender Bias for Natural Language Processing

[Marta Ruiz]

1st CFP NAACL 2022 4th  Workshop on Gender Bias for Natural Language
Processing

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.


Our proposal follows up three successful previous editions of the Workshop
collocated with ACL 2019, COLING 2020, and ACL-IJCNLP 2021, respectively.
As in the two previous years (2020 and 2021), special efforts will be made
this year to encourage a careful and reflective approach to gender bias by
the means of separately reviewed bias statements (Blodgett et al., 2020;
Hardmeier et al., 2021). 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. We will
keep 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, 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.



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 NAACL
2022 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 behaviours are considered as bias in the work and (b) why those
behaviours 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
Apr 8, 2022: Workshop Paper Due Date
May 6, 2022: Notification of Acceptance
May 20, 2022: Camera-ready papers due
July 14 or 15, 2022: Workshop Dates



Keynote

Kellie Webster, Research Scientist at Google Research

Organizers


Marta R. Costa-jussà, Meta AI, Paris
Christian Hardmeier, Uppsala University
Hila Gonen, FAIR and University of Washington
Christine Basta, Universitat Politècnica de Catalunya, Barcelona
Gabriel Stanovsky, Hebrew University of Jerusalem
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