36.310, Confs: Computational Linguistics; General Linguistics; Sociolinguistics; Text/Corpus Linguistics / USA

[The LINGUIST List]

LINGUIST List: Vol-36-310. Wed Jan 22 2025. ISSN: 1069 - 4875.

Subject: 36.310, Confs: Computational Linguistics; General Linguistics; Sociolinguistics; Text/Corpus Linguistics / USA

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Date: 22-Jan-2025
From: Marina Zhukova [mzhukova at ucsb.edu]
Subject: 7th Workshop on Computational Approaches to Linguistic Code-Switching @ NAACL 2025 (CALCS)


7th Workshop on Computational Approaches to Linguistic Code-Switching
@ NAACL 2025 (CALCS)

Date: 03-May-2025 - 04-May-2025
Location: Albuquerque, New Mexico, USA
Contact: Marina Zhukova
Contact Email: mzhukova at ucsb.edu
Meeting URL: https://code-switching.github.io/2025

Linguistic Field(s): Computational Linguistics; General Linguistics;
Sociolinguistics; Text/Corpus Linguistics

Title: 7th Workshop on Computational Approaches to Linguistic
Code-Switching
Short Title: CALCS
Location: NAACL 2025
Website: https://code-switching.github.io/2025
Contact:
Genta Winata, genta.winata at capitalone.com
Sudipta Kar, sudipta.kar.8080 at gmail.com
Marina Zhukova, mzhukova at ucsb.edu
1st Call For Papers: 7th Workshop on Computational Approaches to
Linguistic Code-Switching
Submission Deadline:  Friday, 7 February 2025
We invite submissions for the Workshop on Computational Approaches to
Linguistic Code-Switching. This edition will be the seventh edition of
the workshop that is collocated with NAACL 2025.
Bilingual and multilingual speakers often engage in code-switching
(CS), mixing languages within a conversation, influenced by cultural
nuances. CS can occur at inter-sentential, intra-sentential, and
morphological levels, posing challenges for language understanding and
generation. Models trained for a single language often struggle with
mixed-language input. Despite advances in multilingual pre-trained
language models (LMs), they may still perform poorly on CS data.
Research on LMs' ability to process CS data, considering cultural
nuances, reasoning, coverage, and performance biases, remains
underexplored.
As CS becomes more common in informal communication like newsgroups,
tweets, and social media, research on LMs processing mixed-language
data is urgently needed. This workshop aims to unite researchers
working on spoken and written CS technologies, promoting collaboration
to improve AI's handling of CS across diverse linguistic contexts.
Topics of Interest
The workshop invites contributions from researchers working in NLP and
speech approaches for the analysis and processing of mixed-language
data.
Topics of relevance to the workshop include the following:
- Development of data and model resources to support research on CS
data
- New data augmentation techniques for improving robustness on CS data
- New approaches for NLP downstream tasks: question answering,
conversational agents, named entity recognition, sentiment analysis,
machine translation, language generation, and ASR in CS data
- NLP techniques for the syntactic analysis of CS data
- Domain, dialect, genre adaptation techniques applied to CS data
processing
- Language modeling approaches to CS data processing
- Sociolinguistic and/or sociopragmatic aspects of CS
- Techniques and metrics for automatically evaluating synthetically
generated CS text
- Utilization of LLMs and assessment of their performance on NLP tasks
for CS data
- Survey and position papers discussing the challenges of CS data to
NLP techniques
- Ethical issues and consideration on CS applications
Important Dates:
- Workshop submission deadline (regular and non-archival submissions):
7 February 2025
- Notification of acceptance: 8 March 2025
- Camera ready papers due: 17 March 2025
- Workshop date: 3/4 May 2025
All deadlines are 11.59 pm UTC -12h (“anywhere on Earth”).
Submission Portal:
https://openreview.net/group?id=aclweb.org/NAACL/2025/Workshop/CALCS
Shared Task:
We are also organizing a shared task competition focused on
automatically evaluating synthetically generated CS text. Automatic CS
text generation is valuable for various tasks, especially given the
scarcity of such data. Data augmentation has proven effective in
improving model performance across tasks and languages. Furthermore,
the need for generating CS text in dialogue systems has been
emphasized by the benefits achieved in enabling chatbots to produce
code-switching. As the demand for generating CS text increases, robust
evaluation methods are essential to assess the quality of generations
in terms of accuracy and fluency. This area still lacks sufficient
research in data and methodologies. Our shared task aims to enable
further progress in this field.
We invite all interested peers to get in touch about participation and
follow the website for further updates. A separate CFP will be sent
out for the shared task.
Organizing Committee:
Barid Xi Ai, National University of Singapore
Injy Hamed, MBZUAI
Mahardika Krisna Ihsani, MBZUAI
Sudipta Kar, Amazon Alexa AI
Garry Kuwanto, Boston University
Thamar Solorio, MBZUAI and University of Houston
Derry Tanti Wijaya, Monash University, Indonesia
Genta Indra Winata, Capital One AI Foundations
Marina Zhukova, University of California, Santa Barbara



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