34.1742, Calls: 1st Workshop on Biased Data in Conversational Agents

[The LINGUIST List]

LINGUIST List: Vol-34-1742. Thu Jun 01 2023. ISSN: 1069 - 4875.

Subject: 34.1742, Calls: 1st Workshop on Biased Data in Conversational Agents

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================================================================


Date: 01-Jun-2023
From: Francesca Grasso [fr.grasoso at unito.it]
Subject: 1st Workshop on Biased Data in Conversational Agents


Full Title: 1st Workshop on Biased Data in Conversational Agents
Short Title: BDCA 2023

Date: 18-Sep-2023 - 22-Sep-2023
Location: Turin, Italy
Contact Person: Francesca Grasso
Meeting Email: fr.grasso at unito.it
Web Site: https://bit.ly/BDCA2023

Linguistic Field(s): Computational Linguistics

Call Deadline: 12-Jun-2023

Meeting Description:

The 1st Workshop on Biased Data in Conversational Agents aims to
address the challenges posed by biased data in both Machine Learning
and society. Conversational Agents (CAs) have become prevalent in
various aspects of our daily interactions, but the data used to train
these agents can introduce biases related to racial, sexual,
political, or gender matters. Such biases amplify the potential risks
that CAs pose to society. This workshop invites researchers to compare
different chatbots/corpora, study methods to create or mitigate bias
in datasets, assess and remove bias in corpora, and handle bias at the
chatbot level through NLP or Machine Learning techniques. Theoretical
approaches to addressing bias in CAs are also welcomed.

Call for Papers:

======
TOPICS
======
Topics of interest include but are not limited to:

∑ Comparison and Evaluation of Corpora/Conversational Agents on biased
data
∑ Assessing and mitigating biased data in corpora
∑ Personalized NLP and Information Retrieval
∑ Sexist, Racist, Political, and Gender Dictionary and Ontology
∑ NLP and Machine Learning methods to recognize and handle biased data
∑ Impact of biased data on Conversational Agents
∑ Topic recognition and repair strategies in biased conversations
∑ Mental Models for biased data
∑ Corpora creation and corpora annotation (automatic methods are
accepted)

===========
SUBMISSIONS
===========
Authors are invited to submit original, previously unpublished
research papers. We encourage the submission of:

(A) Extended abstracts: 2 pages
(B) Short papers: 5 to 7 pages
(C) Regular papers: 8 to 14 pages

The space for references is unlimited. Abstracts and papers must be
written in English and formatted according to the Springer LNCS
guidelines. All papers must be converted to PDF prior to electronic
submission.

Submission site:
https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023

All papers need to be 'best-effort' anonymized. Code and data should
be made available anonymously, for example, in an anonymous GitHub
repository via Anonymous GitHub or in a Dropbox folder. The authors
may have a (non-anonymous) pre-print published online, but it should
not be cited in the submitted paper to preserve anonymity.

At least one author of each accepted paper must have a full
registration and be present to present the paper. Papers without a
full registration or in-presence presentation will not be included in
the post-workshop Springer proceedings.



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