We have centralized many code-switching datasets, including the data from the CALCS series, into a single code-switching benchmark. Please consider using the improved version of the data and the public leaderboards available here: ritual.uh.edu/lince

Attention - Regarding COVID-19

Due to the current situation with COVID 19 LREC and all the associated workshops have been canceled. But we will continue with the review process and we have been told that the proceedings will still be issued. We will be in touch as more information becomes available. In the meantime we expect to send out notifications of the paper submissions as originally planned. We hope everyone stays healthy! Feel free to contact us at codeswitching_workshop@googlegroups.com if you have any questions or concerns.

Workshop Information

This workshop aims to bring together researchers interested in solving the problem and increase community awareness of the possible viable solutions to reduce the complexity of the phenomenon. The workshop invites contributions from researchers working in NLP approaches for the analysis and processing of mixed-language data especially with a focus on intrasentential code-switching. Topics of relevance to the workshop will include the following:

• Development of linguistic resources to support research on code-switched data
• NLP approaches for language identification in code-switched data
• NLP approaches for named entity recognition in code-switched data
• NLP techniques for the syntactic analysis of code-switched data
• NLP techniques for higher level tasks on code-switched data, such as Q&A, language understanding, grounding
• Domain/dialect/genre adaptation techniques applied to code-switched data processing
• Language modeling approaches to code-switched data processing
• Crowdsourcing approaches for the annotation of code-switched data
• Machine translation approaches for code-switched data
• Multimodal approaches to processing code switched data
• Application of low resource processing paradigms to code switch processing
• Position papers discussing the challenges of code-switched data to NLP techniques
• Methods for improving ASR in code switched data
• Survey papers of NLP research for code-switched data
• Sociolinguistic aspects of code-switching
• Sociopragmatic aspects of code-switching

Submission Guidelines

• Authors are invited to submit papers describing original, unpublished work in the topic areas listed above. Full papers should from four to eight pages with unlimited number of pages for references.
• All submissions must be in PDF format and must comply with the official LREC 2020 style guidelines: https://lrec2020.lrec-conf.org/en/submission2020/submission-guidelines/

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 https://www.softconf.com/lrec2020/CS2020/

Theme

This year we propose a theme for the workshop around resources and evaluation metrics and frameworks. The goal of the theme is to disseminate more broadly the data sets that are available for the research community, and to engage the community in a discussion about adopting best practices and common frameworks to enable a comprehensive evaluation of technology for code-switched data. We welcome submissions responsive to the theme, in addition to the topics listed above.

Invited Speakers

Alan W. Black - College: Carnegie Mellon University

Updates will be given through the workshop Google group: codeswitching_workshop@googlegroups.com, and the Twitter account: @WCALCS. Direct updates will be sent by email to the participants based on the information provided in the registration form.