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.
CoNLL 2023 Chairs and Organizers
The conference's co-chairs are:
- David Reitter (Google DeepMind, New York City)
- Jing Jiang (Singapore Management University, Singapore) [local chair]
- Shumin Deng (National University of Singapore)
- SIGNLL President: Julia Hockenmaier (University of Illinois at Urbana-Champaign, USA)
- SIGNLL Secretary: Afra Alishahi (Tilburg University, Netherlands)
2 May 2023: The call for papers is now available.
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
- 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, according to the 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.)
- Anonymity period begins: May 30, 2022
- 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
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.