ACL 2025 - Call for System Demonstrations
The ACL 2025 System Demonstration Program Committee invites proposals for the Demonstrations Program. Demonstrations may range from early research prototypes to mature production-ready systems. Publicly available open-source or open-access systems are of special interest. We additionally strongly encourage demonstrations of industrial systems that are technologically innovative given the current state of the art of theory and applied research in natural language processing.
Areas of interest include all topics related to theoretical and applied natural language processing, such as (but not limited to) the topics listed on the main conference website.
Submitted systems may be of the following types:
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Natural language processing systems or system components *
Application systems using language technology components *
Software tools for natural language processing research *
Software for demonstration or evaluation *
Software supporting learning or education *
Tools for data visualization and annotation *
Tools for model inspection *
Development tools
Papers describing accepted demonstrations will be published in a companion volume of the ACL 2025 conference proceedings. We expect at least one of the authors to present a live demo during a demo session at ACL 2025 in Vienna, with an accompanying poster. Please note: Commercial sales and marketing activities are not appropriate in the Demonstrations Program and should be arranged as part of the Exhibit Program
Check the full Call at:
https://2025.aclweb.org/calls/system_demonstration/ [1]
Link to submission system:
https://openreview.net/group?id=aclweb.org/ACL/2025/Demo [2]
Links: ------ [1] https://2025.aclweb.org/calls/system_demonstration/ [2] https://openreview.net/group?id=aclweb.org/ACL/2025/Demo
*CALL FOR PAPERS*
*Special Issue of The Journal of Asia TEFL *(e-ISSN 2466-1511, ISSN 1738-3102, Indexed in SCOPUS, ESCI)
*Learner Corpus Research in the AI Era: Perspectives from Asia*
The emergence of generative AI has fundamentally transformed the landscape of corpus linguistics, particularly in the domain of learner corpus research. These powerful technologies are not merely new analytical tools but represent a paradigm shift in how we conceptualise, collect, and interpret learner language data. As large language models become increasingly embedded in language learning environments, researchers must critically examine both the opportunities and challenges they present.
In Asian contexts, where technological adoption in education proceeds at remarkable pace, there is an urgent need to investigate how these developments are reshaping our understanding of learner language. This special issue aims to bring together cutting-edge research that explores these transformations from theoretical, methodological and practical perspectives.
*1. RESEARCH FOCUS*
This special issue invites original contributions that examine how generative AI is reconfiguring learner corpus research. We are particularly interested in empirical studies that demonstrate innovative approaches to corpus compilation, annotation and analysis in the AI era. Successful submissions will offer insights into how corpus linguistics methodologies are adapting to accommodate AI-mediated language learning environments.
The integration of AI technologies raises fundamental questions about the nature of learner language itself. How do we distinguish between authentic learner production and AI-assisted output? What new analytical frameworks are required to interpret learner corpora in contexts where AI tools are ubiquitous? How might AI-enhanced analysis reveal patterns in learner language previously undetectable through conventional methods?
*Potential topics include:*
- Novel approaches to learner corpus compilation and annotation leveraging AI technologies - Methodological innovations in error analysis and pattern identification using AI - Comparative investigations of AI-generated versus authentic learner language - Applications of AI-driven corpus analysis in developing targeted pedagogical interventions - Validity and reliability concerns in corpus research within AI-integrated learning environments - Corpus-informed evaluations of AI feedback systems in language learning contexts
*2. SUBMISSION REQUIREMENTS*
We welcome empirical studies, methodological papers, and critical analyses that substantively advance our understanding of learner corpus research in the AI era. Submissions should demonstrate technical rigour while addressing practical implications for language teaching and assessment in Asian contexts. Papers should engage critically with existing corpus linguistics methodologies while proposing adaptations necessary for the AI era.
*3. IMPORTANT DATES*
- *Abstract Submission Deadline: April 7, 2025* - Notification of Acceptance: April 30, 2025 - Full Paper Submission Deadline: November 28, 2025 - Reviews by Reviewers: December 2025 - First Revisions by Authors: January 2026 - Reviews by the Editor: March 2026 - Second Revisions by Authors: April 2026 - Editing for Publishing: April 28 – May 23, 2026 - Expected Publication Date: May 26, 2026
*4. ABSTRACT SUBMISSION GUIDELINES*
Abstracts should present a clear articulation of research questions, methodological framework, and the significance of the study to learner corpus research in the AI era. Effective abstracts will demonstrate precision in language and conceptual clarity while highlighting the innovative aspects of the research. Abstracts should not exceed 500 words and must be submitted by April 7, 2025 through the following link:
*Abstract submission (Deadline: April 7, 2025)*: https://forms.gle/njjoaBCuv4mGnzUz9
*5. FULL PAPER SUBMISSION GUIDELINES*
Authors of accepted abstracts should prepare their manuscripts following The Journal of Asia TEFL guidelines. Full papers must be submitted through the journal's online submission system and will undergo a rigorous double-blind peer review process. Successful papers will present compelling evidence and incisive analysis of how AI technologies are transforming corpus linguistics methodologies and applications. Papers should demonstrate meticulous attention to data collection procedures, analytical frameworks, and the implications of findings for both theory and practice in learner corpus research.
For inquiries regarding this special issue, please contact the guest editor, CK Jung, at ckjung@inu.ac.kr.
We look forward to receiving your contributions to this timely exploration of how generative AI is reshaping the field of learner corpus research.
*CK Jung BEng(Hons) Birmingham MSc Warwick EdD Warwick Cert Oxford*
Associate Professor | Department of English Language and Literature, Incheon National University, South Korea
Director | Institute for Corpus Research, Incheon National University, South Korea
Editor-in-Chief | Asia Pacific Journal of Corpus Research, South Korea
Editorial Board | Corpora, Edinburgh University Press, UK
Editorial Board | English Today, Cambridge University Press, UK
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ACL Announcements -
Prof CK Jung