Main Conference

ACL 2024

Submission Deadline: 15 February 2024 (February ARR cycle)
Conference Dates: August 11-16 2024
Location: Bangkok, Thailand
Special Theme: “Open science, open data, and open models for reproducible NLP research”

  • Claire Gardent (General Chair)
  • Lun-Wei Ku, André Martins, Vivek Srikumar (Program Chairs):

For questions related to paper submission, email:
For all other questions, email:

Call for Main Conference Papers

ACL 2024 invites the submission of long and short papers featuring substantial, original, and unpublished research in all aspects of Computational Linguistics and Natural Language Processing. As in recent years, some of the presentations at the conference will be of papers accepted by the Transactions of the ACL (TACL) and by the Computational Linguistics (CL) journals.

Papers submitted to ACL 2024, but not selected for the main conference, will also automatically be considered for publication in the Findings of the Association of Computational Linguistics.

Important Dates

Anonymity period: ACL changed its policy for review and citation on January 12, 2022 As a result, no anonymity period will be required for papers submitted for the Feb. 1, 2024 TACL deadline or the Feb. 15, 2024 ARR deadline The submissions themselves must still be fully anonymized.

Please see for details.

Submission deadline (all papers are submitted to ARR) February 15, 2024

Papers submitted to ARR no later than February 15, 2024 will have reviews and meta-reviews by April 15, 2024, in time for the ACL 2024 commitment deadline (see below). At submission time to ARR, authors will be asked to select one preferred venue to calculate the acceptance rate. However, selecting ACL 2024 as a preferred venue does not require authors to commit to ACL 2024.

ARR reviews & meta-reviews available to authors of February cycle April 15, 2024
Commitment deadline for ACL 2024 April 20, 2024

Deadline for authors to commit their reviewed papers, reviews, and meta-review to ACL 2024. It is not necessary to have selected ACL as a preferred venue during submission.

Notification of acceptance May 15, 2024
Withdrawal deadline June 5, 2024
Camera-ready papers due June 5, 2024
Tutorials August 11, 2024
Conference August 12-14, 2024
Workshops August 15-16, 2024

All deadlines are 11:59PM UTC-12:00 (“anywhere on Earth”).

Submission Topics

ACL 2024 aims to have a broad technical program. Relevant topics for the conference include, but are not limited to, the following areas (in alphabetical order):

  • Computational Social Science and Cultural Analytics
  • Dialogue and Interactive Systems
  • Discourse and Pragmatics
  • Efficient/Low-Resource Methods for NLP
  • Ethics, Bias, and Fairness
  • Generation
  • Information Extraction
  • Information Retrieval and Text Mining
  • Interpretability and Analysis of Models for NLP
  • Linguistic theories, Cognitive Modeling and Psycholinguistics
  • Machine Learning for NLP
  • Machine Translation
  • Multilinguality and Language Diversity
  • Multimodality and Language Grounding to Vision, Robotics and Beyond
  • NLP Applications
  • Phonology, Morphology and Word Segmentation
  • Question Answering
  • Resources and Evaluation
  • Semantics: Lexical
  • Semantics: Sentence-level Semantics, Textual Inference and Other areas
  • Sentiment Analysis, Stylistic Analysis, and Argument Mining
  • Speech recognition, text-to-speech and spoken language understanding
  • Summarization
  • Syntax: Tagging, Chunking and Parsing

Paper Submission Details

Papers must be submitted by the ARR’s February 2024 cycle. Papers submitted to one of the earlier ARR deadlines are also eligible, and it is not necessary to (re) submit on the current cycle.

Both long and short paper submissions should follow all of the ARR submission requirements, including:

  • Long Papers and Short Papers
  • Anonymity Period and Instructions for Two-Way Anonymized Review
  • Authorship
  • Citation and Comparison
  • Multiple Submission Policy, Resubmission Policy, and Withdrawal Policy
  • Ethics Policy
  • Limitations
  • Paper Submission and Templates
  • Optional Supplementary Materials

Papers should be submitted to one of the ARR 2023 submission sites.

Final versions of accepted papers will be given one additional page of content (up to 9 pages for long papers, up to 5 pages for short papers) to address reviewers’ comments.

Theme Track: Open science, open data, and open models for reproducible NLP research

Following the success of the ACL 2020-2023 Theme tracks, we are happy to announce that ACL 2024 will have a new theme with the goal of reflecting and stimulating discussion about open science and reproducible NLP research, as well as supporting the open source software movement. We encourage contributions related to the release of high quality datasets, novel ideas for evaluation, non-trivial algorithm and toolbox implementations, and models which are properly documented (e.g. via model cards). We believe this topic is very timely and addresses a growing concern from NLP researchers. The advent of large language models as a general purpose tool for NLP, often served as closed APIs, without public information about training data and model size, perhaps even containing test data, makes it very hard to reproduce prior work and compare fairly and rigorously with newly developed models and techniques. This brings the serious risk of hindering progress in the field. With this theme track we seek a discussion on increased transparency in the field by promoting the use of open models and open-source initiatives in NLP as an alternative to closed approaches.

The theme track invites empirical and theoretical research, descriptions and release of high quality open datasets, open models, and open source software implementations, as well position and survey papers reflecting on the ways in which open data, open models, and open-source initiative can contribute to advances in the field. The possible topics of discussion include (but are not limited to) the following:

  • What are the advantages (and risks, if any) of making available open-source software, open datasets and models to the research community? What are the risks (scientific and societal) of not making them available?
  • What kind of incentive mechanisms should be in place to encourage the creation by the research community of open, high-quality datasets and models and their adoption in experimental workflows?
  • What elements of open releases (e.g. documentation, cards, licensing, testing) are essential or should be highly recommended in order to be scientifically useful and adopted by the community?

The theme track submissions can be either long or short.