For questions related to paper submission, email: editors aclrollingreview. For all other questions, email: aclprogramchairs googlegroups. ACL invites the submission of long and short papers featuring substantial, original, and unpublished research in all aspects of Computational Linguistics and Natural Language Processing. Papers submitted to ACL , but not selected for the main conference, will also automatically be considered for publication in the Findings of the Association of Computational Linguistics.
As a result, no anonymity period will be required for papers submitted for the Feb. The submissions themselves must still be fully anonymized. Papers submitted to ARR no later than February 15, will have reviews and meta-reviews by April 15, , in time for the ACL commitment deadline see below. At submission time to ARR, authors will be asked to select one preferred venue to calculate the acceptance rate. Deadline for authors to commit their reviewed papers, reviews, and meta-review to ACL It is not necessary to have selected ACL as a preferred venue during submission.
ACL aims to have a broad technical program. Relevant topics for the conference include, but are not limited to, the following areas in alphabetical order :. Papers submitted to one of the earlier ARR deadlines are also eligible, and it is not necessary to re submit on the current cycle. Both long and short paper submissions should follow all of the ARR submission requirements, including:. Following the success of the ACL Theme tracks, we are happy to announce that ACL will have a new theme with the goal of reflecting and stimulating discussion about open science and reproducible NLP research, as well as supporting the open source software movement.
We encourage contributions related to the release of high quality datasets, novel ideas for evaluation, non-trivial algorithm and toolbox implementations, and models which are properly documented e.
We believe this topic is very timely and addresses a growing concern from NLP researchers. The advent of large language models as a general purpose tool for NLP, often served as closed APIs, without public information about training data and model size, perhaps even containing test data, makes it very hard to reproduce prior work and compare fairly and rigorously with newly developed models and techniques.
