The 2022 SIGNLL Conference on Computational Natural Language Learning
(CoNLL 2022, Co-located with EMNLP 2022)
Website: https://conll.org/

SIGNLL invites submissions to the 26th Conference on Computational Natural 
Language Learning (CoNLL 2022). 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 2022 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 2022 guidelines. Please refer to 
the EMNLP 2022 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 2022 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, 2022.

Multiple submission policy

CoNLL 2022 will not accept papers that are currently under submission, or that 
will be submitted to other meetings or publications, including EMNLP. Papers 
submitted elsewhere as well as 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 2022 must ensure that 
the submissions do not overlap significantly (>25%) with each other in content 
or results.

CoNLL 2022 has the same policy as EMNLP 2022 regarding ARR submissions. This 
means that CoNLL 2022 will also accept submissions of ARR-reviewed papers, 
provided that the ARR reviews and meta-reviews are available by the ARR 
commitment deadline. We follow the EMNLP policy for papers that were previously 
submitted to ARR, or significantly overlap (>25%) with such submissions.

Important Dates

Anonymity period begins: May 30th, 2022
Submission deadline for START direct submissions: Thursday June 30th, 2022
Commitment deadline for ARR papers: August 1st, 2022
Notification of acceptance: Mid-September, 2022
Camera ready papers due: October 15th, 2022
Conference: December 7th, 8th, 2022
All deadlines are at 11:59pm UTC-12h ("anywhere on earth").
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