COLING 2020 : INDUSTRY TRACK CALL FOR PAPERS
Call For Papers
The 28th International Conference on Computational Linguistics (COLING 2020) will take place in Barcelona from September 15 to 18, 2020. The Industry Track will take place as a part of the main conference and welcomes submission of long and short papers.
The balance between excellent research in academia and industry has strongly shifted in the last ten years, with industry attracting many of the best talents and industrial research making strong contributions to progress in our field – both from industrial research departments that contribute to the advancement of CL and also knowledgeable inventors and developers of innovative language and speech products.
This session will showcase commercially-driven research, from a diverse range of angles, including challenges of algorithmic fairness, privacy, production scalability, and a shifting data landscape. The goals of this session are to foster connections between industry NLP practitioners, to share insights from industry research to the broader community, and to increase engagement with academia on research questions of high priority in industry.
The Industry Track invites submission of long and short papers in all topic areas of COLING 2020 that focus on the real-world application and deployment of NLP and computational linguistic advances. We are particularly interested in the following:
Challenges of doing applied research at scale
Noisy and/or unpredictable data (real world v. contrived)
Negative results related to industry applications
Analysis, modeling, and dataset construction under the constraint of respecting data privacy
Algorithmic ethics and responsibility
Evaluation methodologies, particularly for monitoring performance after deployment
Trade-offs between resources (environmental and production) and performance; data size and modeling improvements
Towards replicability in deep learning: experimental procedures necessary to develop successful models (e.g., data preparation and parameter tuning)
Submissions on novel tasks are welcome, and we particularly encourage authors to provide representative data samples.
We invite submissions of up to nine (9) pages maximum, plus bibliography for long papers and four (4) pages, plus bibliography, for short papers. The COLING’2020 templates must be used; these are provided in LaTeX and also Microsoft Word format (https://coling2020.org/coling2020.zip). Submissions will only be accepted in PDF format. Deviations from the provided templates will result in rejections without review. Submit papers by the end of the deadline day (timezone is UTC-12) via our Softconf submission site: http://softconf.com/coling2020/industry
Additionally, authors should ensure the following:
Papers must be of original, previously-unpublished work. Papers must be anonymized to support double-blind reviewing. If the paper is available as a preprint, this must be indicated on the submission form but not in the paper itself. In addition, COLING’2020 will follow the same policy as ACL’2018 establishing an anonymity period (from submission to author notification) during which non-anonymous posting of preprints is not allowed. Also included in that policy are instructions to reviewers to not rate papers down for not citing recent preprints. Authors are asked to cite published versions of papers instead of preprint version when possible.
Papers that have been or will be under consideration for other venues at the same time must be declared at submission time. If a paper is accepted for publication at COLING, it must be immediately withdrawn from other venues. If a paper under review at COLING is accepted elsewhere and authors intend to proceed there, the COLING committee must be notified immediately.
Please note that since this track will be a part of the main conference, accepted papers will be archival.
April 8, 2020: Submission deadline
June 10, 2020: Notification of acceptance
June 30, 2020: Camera-ready papers due
September 15-18, 2020: COLING 2020
Ann Clifton, Spotify Courtney Napoles, Grammarly