36.647, Calls: 7th Workshop on Computational Approaches to Linguistic Code-Switching @ NAACL 2025 (CALCS) / USA

[The LINGUIST List]

LINGUIST List: Vol-36-647. Wed Feb 19 2025. ISSN: 1069 - 4875.

Subject: 36.647, Calls: 7th Workshop on Computational Approaches to Linguistic Code-Switching @ NAACL 2025 (CALCS) / USA

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Date: 19-Feb-2025
From: Marina Zhukova [mzhukova at ucsb.edu]
Subject: 7th Workshop on Computational Approaches to Linguistic Code-Switching @ NAACL 2025 (CALCS)


Full Title: 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 Person: Marina Zhukova
Meeting Email: mzhukova at ucsb.edu

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

Call Deadline: 21-Feb-2025

Call for Papers:
We are excited to announce that the paper submission deadline has been
extended to February 21! We also released the Shared Task on Automatic
Evaluation for Code-Switched Text Generation.
This edition will be the seventh edition of the workshop collocated
with NAACL 2025: https://code-switching.github.io/
Submissions:
The workshop accepts three categories of papers: regular workshop
papers, non-archival and cross-submissions. Only regular workshop
papers will be included in the proceedings as archival publications.
The regular workshop papers are eligible for the best paper award. All
three categories of papers may be long (maximum 8 pages plus
references) or short (maximum 4 pages plus references), with unlimited
additional pages for references, following the ARR formatting
requirements. The reported research should be substantially original.
Accepted papers will be presented as posters and orals. Reviewing will
be double-blind, and thus, no author information should be included in
the papers; self-reference that identifies the authors should be
avoided or anonymized. Accepted regular workshop papers will appear in
the workshop proceedings. We welcome papers with a maximum of 2 pages
for non-archival submission. Please send us an email if you are
submitting the non-archival submission. The limitation section is
optional and will not be counted in the page limit. The submission
portal is open on OpenReview.
Shared Task on Automatic Evaluation for Code-Switched Text Generation
This shared task focuses on developing automatic evaluation metrics
for code-switched (CS) text generation. Participants are tasked with
creating systems that can accurately assess the quality of
synthetically generated CS text, considering both fluency and
accuracy. This is crucial because:
 - Scarcity of CS Data: CS text data is limited, making automatic
generation vital for data augmentation and improving model
performance.
 - Growing Demand: The need for CS text is increasing, particularly in
dialogue systems and chatbots, to enable more natural and inclusive
interactions.
 - Lack of Robust Evaluation: Current methods for evaluating CS text
are insufficient, hindering progress in this field.
This shared task aims to address this gap and drive further research
in automatic evaluation metrics for CS text generation.
Languages Supported:
Public Leaderboard: English-Hindi, English-Tamil, English-Malayalam
Private Leaderboard: English-Indonesian, Indonesian-Javanese, Singlish
(English-Chinese)
Metric:
Accuracy: Systems will be evaluated based on their accuracy in
predicting human preferences for CS text. This will be measured by
comparing the system's ranking of generated sentences (Sent 1 vs. Sent
2) with human annotations in the CSPref dataset.
Dataset:
The CSPref dataset will be used for this task. It contains:
Original L1: English sentences
Original L2: Hindi, Tamil, or Malayalam sentences
Sent 1, Sent 2: Two different CS generations based on the original
sentences.
Chosen: Human annotation indicating the preferred sentence (Sent 1,
Sent 2, or Tie).
Lang: Language pair
Data is available here:
https://huggingface.co/datasets/garrykuwanto/cspref
Evaluation:
Systems will be ranked on a public leaderboard based on their accuracy
in predicting human preferences on the English-Hindi, English-Tamil,
and English-Malayalam language pairs. A private leaderboard will
evaluate system performance on unseen language pairs
(English-Indonesian, Indonesian-Javanese, Singlish) to assess
generalization ability.
Submission:
Participants will submit their system's predictions for each instance
in the test set, indicating their preferred sentence (Sent 1, Sent 2,
or Tie).
Goal:
The goal of this shared task is to encourage the development of robust
and reliable automatic evaluation metrics for CS text generation,
ultimately leading to more fluent and accurate CS language models.
Competition Page:
For more information about the competition, please visit the
competition page:
https://eval.ai/web/challenges/challenge-page/2437/overview
See updated timelines below:
Paper Submission
Workshop submission deadline (regular and non-archival submissions):
21 February 2025
Notification of acceptance: 8 March 2025
Camera ready papers due: 17 March 2025
Workshop date: 3/4 May 2025
Shared Task Submission
Train release and platform: 23 January 2025
Test release: 14 February 2025
Results submission: 21 February 2025
Paper submission: 28 February 2025
Notification deadline: 8 March 2025
All deadlines are 11.59 pm UTC -12h (“anywhere on Earth”).



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