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.
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").
The independent research group Trustworthy Human Language Technologies (TrustHLT) at the Department of Computer Science of the Technical University of Darmstadt, Germany has a job opening for a
Postdoctoral researcher (m/f/d)
in the recently acquired project “PrivaLingo: Truly Privacy-Preserving Machine Translation” by Dr. Ivan Habernal.
This position will focus on a broad range of research questions related to privacy-preserving NLP models with a special focus on neural machine translation. A solid background in machine learning for natural language processing is essential, prior experience with differential privacy in model training is a plus. Wages and salaries are based on the collective agreement applicable to the TU Darmstadt (TV-TU Darmstadt). The starting date is as soon as possible, the position is funded until April 2024.
Candidates
The ideal candidate hold a PhD degree in computer science, computational linguistics, machine learning, or a related discipline, has a strong interest in privacy in natural language processing, excellent analytical and programming skills, is a team player, and is fluent in English.
Diversity
TU Darmstadt is strongly committed to diversity and particularly welcomes applications from members of underrepresented groups. Applications from female candidates are highly encouraged.
Team
TrustHLT is an independent research group led by Dr. Ivan Habernal, appointed at the Department of Computer Science of the Technical University of Darmstadt. The group conducts research in the field of natural language processing with a focus on privacy-preserving technologies and legal argumentation, see www.trusthlt.org for more details. The Department of Computer Science at TU Darmstadt regularly ranks among the top in Germany.
Application
Please send your detailed CV including a publication list, names of two referees, and a letter of motivation outlining your research interests to ivan.habernal(a)tu-darmstadt.de, subject: “Postdoc application PrivaLingo”. Send all documents as a single PDF (“PDF Arranger” is a helpful tool, for instance). Please do not hesitate to contact Dr. Habernal should you have any further questions. Deadline for applications is June 30, 2022. Applications arriving after the deadline will still be considered if the position is not filled yet.