CoNLL 2023

May 2, 2023

CoNLL is a yearly conference organized by SIGNLL (ACL's Special Interest Group on Natural Language Learning), focusing on theoretically, cognitively and scientifically motivated approaches to computational linguistics. This year, CoNLL will be colocated with EMNLP 2023. Registrations for CoNLL can be made through EMNLP (workshop 1).

Submission page

CoNLL 2023 Chairs and Organizers

The conference's co-chairs are:

Publication chair:


  • SIGNLL President: Julia Hockenmaier (University of Illinois at Urbana-Champaign, USA)
  • SIGNLL Secretary: Afra Alishahi (Tilburg University, Netherlands)

Best paper

ChiSCor: A Corpus of Freely-Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science
Bram van Dijk, Max van Duijn, Suzan Verberne and Marco Spruit

Honorable mentions

Revising with a Backward Glance: Regressions and Skips during Reading as Cognitive Signals for Revision Policies in Incremental Processing
Brielen Madureira, Pelin Çelikkol and David Schlangen

The Validity of Evaluation Results: Assessing Concurrence Across Compositionality Benchmarks
Kaiser Sun, Adina Williams and Dieuwke Hupkes

Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning
Lucas Weber, Elia Bruni and Dieuwke Hupkes


The program is available here.


27 November 2023: The program is now available.

7 November 2023: Information (presentation format) regarding accepted papers has been sent out to authors.

2 May 2023: The call for papers is now available.

Areas and ACs

  • Computational Psycholinguistics, Cognition and Linguistics: Mary A. Kelly
  • Computational Social Science: Wei Gao, Jana Diesner
  • Interaction and Grounded Language Learning: Hao Tan
  • Lexical, Compositional and Discourse Semantics: Shane Steinert-Threlkeld
  • Multilingual Work and Translation: Maja Popović
  • Natural Language Generation: Fei Liu
  • Resources and Tools for Scientifically Motivated Research: Sebastian Gehrmann
  • Speech and Phonology: Kyle Gorman
  • Syntax and Morphology: Ryan Cotterell
  • Theoretical Analysis and Interpretation of ML Models for NLP: Dieuwke Hupkes, Kevin Small

BabyLM Challenge

The BabyLM Challenge is the shared task at CoNLL 2023.

Call For Papers

SIGNLL invites submissions to the 27th Conference on Computational Natural Language Learning (CoNLL 2023). The focus of CoNLL is on theoretically, cognitively and scientifically motivated approaches to computational linguistics, rather than on work driven by particular engineering applications. Such approaches include:

  • Computational learning theory and other techniques for theoretical analysis of machine learning models for NLP
  • Models of first, second and bilingual language acquisition by humans
  • Models of language evolution and change
  • Computational simulation and analysis of findings from psycholinguistic and neurolinguistic experiments
  • Analysis and interpretation of NLP models, using methods inspired by cognitive science or linguistics or other methods
  • Data resources, techniques and tools for scientifically-oriented research in computational linguistics
  • Connections between computational models and formal languages or linguistic theories
  • Linguistic typology, translation, and other multilingual work
  • Theoretically, cognitively and scientifically motivated approaches to text generation

We welcome work targeting any aspect of language, including:

  • Speech and phonology
  • Syntax and morphology
  • Lexical, compositional and discourse semantics
  • Dialogue and interactive language use
  • Sociolinguistics
  • Multimodal and grounded language learning

We do not restrict the topic of submissions to fall into this list. However, the submissions’ relevance to the conference’s focus on theoretically, cognitively and scientifically motivated approaches will play an important role in the review process.

Submitted papers must be anonymous and use the EMNLP 2023 template. Submitted papers may consist of up to 8 pages of content plus unlimited space for references. Authors of accepted papers will have an additional page to address reviewers’ comments in the camera-ready version (9 pages of content in total, excluding references). Optional anonymized supplementary materials and a PDF appendix are allowed. The appendix should be submitted as a separate PDF file (different from EMNLP 2023 guidelines). Please refer to the EMNLP 2023 Call for Papers for more details on the submission format. Submission is electronic, using the Softconf START conference management system. Note that, unlike EMNLP, we do not mandate that papers have a section discussion limitations of the work. However, we strongly encourage authors have such a section in the appendix.

CoNLL adheres to the ACL anonymity policy, as described in the EMNLP 2023 Call for Papers. Briefly, non-anonymized manuscripts submitted to CoNLL cannot be posted to preprint websites such as arXiv or advertised on social media after May 30th, 2023.

Please submit via Softconf START. (Note that, unlike EMNLP 2023, CoNLL 2023 will not accept ARR submissions.)

Please note that CoNLL 2023 is an in-person conference. We expect all accepted papers to be presented physically and presenting authors must register through EMNLP (workshop 1).

All deadlines are 11:59pm UTC-12h (anywhere on Earth)

  • Anonymity period begins: May 30, 2023
  • Submission deadline: Friday June 30, 2023
  • Notification of acceptance: Friday, October 6, 2023
  • Camera ready papers due: Friday, October 20, 2023
  • Conference: December 6 – 7, 2023


CoNLL 2023 will be held in-person, along with EMNLP in Singapore.

Multiple submission policy

CoNLL 2023 will refuse papers that are currently under submission, or that will be submitted to other meetings or publications, including EMNLP. Papers submitted elsewhere and papers that overlap significantly in content or results with papers that will be (or have been) published elsewhere will be rejected. Authors submitting more than one paper to CoNLL 2023 must ensure that the submissions do not overlap significantly (>25%) with each other in content or results. Notwithstanding this policy, double-submission to the BabyLM challenge or GenBench is acceptable.

Conference chairs