Venue to be defined
Computational Approaches to Linguistic Code-Switching, CALCS 2021
Venue to be defined

First Call for Papers

Multilingual speakers will often mix languages when they communicate with other multilingual speakers in what is usually known as code-switching (CSW). CSW is typically present on the intersentential, intrasentential and even morphological levels. CSW presents serious challenges for language technologies such as Machine Translation (MT), Automatic Speech Recognition (ASR), language generation (LG), information retrieval (IR) and extraction (IE), and semantic processing. Traditional techniques trained for one language quickly break down when there is input mixed in from another. Recent work has shown that even powerful multilingual models, such as multilingual BERT, yield subpar performance on CSW data (cf. Aguilar and Solorio, 2020).

Considering the ubiquitous nature of CSW in informal text communication such as newsgroups, tweets, blogs, and other social media, and the number of multilingual speakers worldwide that use these platforms, addressing the challenge of processing CSW data continues to be of great practical value. This workshop aims to bring together researchers interested in technology for mixed language data, in either spoken or written form, and increase community awareness of the different efforts developed to date in this space.

Topics of Interest

The workshop will invite contributions from researchers working in NLP and speech approaches for the analysis and processing of mixed-language data. Topics of relevance to the workshop will include the following:

  1. Development of linguistic resources to support research on code-switched data;
  2. NLP approaches for any of language identification/named entity recognition/sentiment analysis/machine translation/language generation in code-switched data;
  3. NLP techniques for the syntactic analysis of code-switched data;
  4. Domain/dialect/genre adaptation techniques applied to code-switched data processing;
  5. Language modeling approaches to code-switched data processing;
  6. Crowdsourcing approaches for the annotation of code-switched data;
  7. Position papers discussing the challenges of code-switched data to NLP techniques;
  8. Methods for improving ASR in code switched data;
  9. Survey papers of NLP research for code-switched data;
  10. Sociolinguistic and/or sociopragmatic aspects of code-switching.

Important Dates

  • Workshop submission deadline (long, short and special track): March 29th
  • Notification of acceptance: April 15th
  • Notification of acceptance: April 19th
  • Camera ready papers due: April 26th
  • Workshop date: June 11th

All deadlines are 11.59 pm UTC -12h (“anywhere on Earth”).

Shared Tasks on Machine Translation in Code-Switching Settings

In the past few years we have organized a series of shared tasks focusing primarily on enabling technology for code-switching, including language identification, part of speech tagging and named entity recognition. This year we are organizing a series of shared tasks involving machine translation for code-switching settings in multiple language combinations and directions.

Task 1. Supervised Setting: MT for English → Hinglish

In this task we provide gold standard data to train and evaluate MT models to take English as input and generate Hinglish data.

Task 2. Unsupervised Setting: MT for multiple language combinations

We provide raw data with no gold label translations. Participants are challenged to work on systems that can generate high quality translations in the pairs shown below. More language directions may be added soon:

  • Spanish-English → English
  • Spanish-English → Spanish
  • English → Spanish-English
  • English → Spanish
  • Modern Standard Arabic-Egyptian Arabic → English
  • Modern Standard Arabic-Egyptian Arabic → Spanish

For example:

  • [Spanish-English → English]: I’m expecting dos camonietas llenas de rosas This weekend. → I’m expecting two trucks full of roses This weekend.
  • [Spanish-English → Spanish]: Es viernes y el outfit lo sabe → Es viernes y el atuendo lo sabe
  • [English → Spanish]: My goal is to move to my own apartment next year → Mi objetivo es mudarme a mi propio apartamento el próximo año
  • [Spanish → English]: A mi manera o pa la calle!! → My way or the highway!!

We will use Linguistic Code-Switching Evaluation Benchmark. The leaderboard will rank systems based on BLUE scores. We also plan to do a smaller, human evaluation that will be presented at the workshop.


To access the data sets go here: Linguistic Code-Switching Evaluation Benchmark

[Update (03/29/2021)]: The usernames are removed from the datasets. Please download the newest version of datasets from Lince.

  • Shared Task training data release: Feb 26th
  • Shared Task test phase: April 1st - 7th
  • Shared Task test phase: April 19th - 25th
  • Shared Task System description papers due: April 15th
  • Shared Task System description papers due: April 30th
  • Shared Task reviews back to authors: April 22nd
  • Shared Task reviews back to authors: May 8th
  • Shared Task Camera ready papers due: May 15th

Questions about the shared task can be sent to:


  • Authors are invited to submit papers describing original, unpublished work in the topic areas listed above. Long papers can contain up to eight pages with unlimited number of pages for references, while short papers can include up to four pages of content and unltimited pages for references.
  • All submissions must be in PDF format and must comply with the official NAACL 2021 style guidelines:–requirements
The Review Process

The reviewing process will not be blind and papers can include the authors’ names and affiliations. Each submission will be reviewed by at least three members of the program committee. Accepted papers will be published in the workshop proceedings.

Multiple Submission Policy

Papers that have been or will be submitted to other meetings or publications are acceptable, but authors must indicate this information at submission time. If accepted, authors must notify the organizers before the camera-ready deadline as to whether the paper will be presented at the workshop or elsewhere.

Electronic Submission

Papers should be submitted electronically at

*NEW* Rising Stars Track *NEW*

We also invite non-archival one page abstracts of recently published work highlighting the CSW research by young researchers or early career investigators. The goal is to help increase the visibility of PhD students, Postdocs and early career investigators (loosely defined) working in the space of language technology for CSW. Please note that you should use the anonymized template for submission and you can use unlimited number of pages for references.

Invited Speakers

Ozlem Cetinoglu    University of Stuttgart
Ngoc Thang Vu    University of Stuttgart
Manish Shrivastava    International Institute of Information Technology Hyderabad

Program Committee

Gustavo Aguilar    University of Houston
Elena Álvarez Mellado    University of Southern California
Segun Aroyehun    Insituto Politécnico Nacional
Kalika Bali    Microsoft Research India
Astik Biswas    Oracle
Monojit Choudhury    Microsoft Research India
Amitava Das    Wipro AI Lab
Indranil Dutta    Jadavpur University
Alexander Gelbukh    Insituto Politécnico Nacional
Genta Indra Winata    Hong Kong University of Science and Technology
Sudipta Kar    Amazon
Grandee Lee    National University of Singapore
Els Lefever    Ghent University
Constantine Lignos    University of Pennsylvania
Yang Liu    Amazon
Manuel Mager    Universität Stuttgart
Parth Patwa    Indian Institute of Information Technology Sri City
Sai Krishna Rallabandi    Carnegie Mellon University
Yihong Theis    Kansas State University
Van Tung Pham    Nanyang Technological University
Khyathi Raghavi Chandu    Carnegie Mellon University
Seza Doğruöz    Ghent University


Department of Computer Science
University of Houston
Ph.D. Student
Department of Computer Science
University of Houston
Department of Computer Science
Carnegie Mellon University
Research Scientist, Facebook AI
Professor, Department of Computer Science
George Washington University
Senior Researcher
MSR India
Applied Scientist
Amazon Alexa AI
Advanced Computer Scientist
SRI International
Research Fellow
Microsoft Research India