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UID:/NewsandEvents/Archives/2019/newsitem/10451/1-
 --2-August-2019-1st-ACL-Workshop-on-Gender-Bias-fo
 r-Natural-Language-Processing-Florence-Italy
DTSTAMP:20190401T142115
SUMMARY:1st ACL Workshop on Gender Bias for Natura
 l Language Processing, Florence, Italy
DTSTART;VALUE=DATE:20190801
DTEND;VALUE=DATE:20190802
LOCATION:Florence, Italy
DESCRIPTION: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 pe
 rceived biases, present in widely used products, c
 an lead to poor user experiences. This workshop wi
 ll be the first dedicated to the issue of gender b
 ias in NLP techniques and it includes a shared tas
 k on coreference resolution. In order to make prog
 ress as a field, this workshop will specially focu
 s on discussing and proposing standard tasks which
  quantify bias.  Keynote Speaker: Pascale Fung, Ho
 ng Kong University of Science and Technology  We i
 nvite submissions of technical work exploring the 
 detection, measurement, and mediation of gender bi
 as in NLP models and applications. Other important
  topics are the creation of datasets exploring dem
 ographics such as metrics to identify and assess r
 elevant biases or focusing on fairness in NLP syst
 ems. Finally, the workshop is also open to non-tec
 hnical work welcoming sociological perspectives.  
 We also invite work on gender-fair modeling via ou
 r shared task, coreference resolution on GAP (Webs
 ter et al. 2018). GAP is a coreference dataset des
 igned to highlight current challenges for the reso
 lution of ambiguous pronouns in context. Participa
 tion will be via Kaggle, with submissions open ove
 r a three month period in the lead up to the works
 hop.
X-ALT-DESC;FMTTYPE=text/html:<div>\n  <p>Gender an
 d other demographic biases in machine-learned mode
 ls are of increasing interest to the scientific co
 mmunity and industry. Models of natural language a
 re highly affected by such perceived biases, prese
 nt in widely used products, can lead to poor user 
 experiences. This workshop will be the first dedic
 ated to the issue of gender bias in NLP techniques
  and it includes a shared task on coreference reso
 lution. In order to make progress as a field, this
  workshop will specially focus on discussing and p
 roposing standard tasks which quantify bias.</p>\n
 \n  <p>Keynote Speaker: Pascale Fung, Hong Kong Un
 iversity of Science and Technology</p>\n</div><div
 >\n  <p>We invite submissions of technical work ex
 ploring the detection, measurement, and mediation 
 of gender bias in NLP models and applications. Oth
 er important topics are the creation of datasets e
 xploring demographics such as metrics to identify 
 and assess relevant biases or focusing on fairness
  in NLP systems. Finally, the workshop is also ope
 n to non-technical work welcoming sociological per
 spectives.</p>\n\n  <p>We also invite work on gend
 er-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 pronoun
 s in context. Participation will be via Kaggle, wi
 th submissions open over a three month period in t
 he lead up to the workshop.</p>\n</div>
URL:http://genderbiasnlp.talp.cat
CONTACT:Marta R. Costa-jussà at marta.ruiz at upc.
 edu
CONTACT:Kellie Webster at websterk at google.com
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