Submission topics: Definitely need to review this together (potentially see what papers were on what tracks in prev years), however some do stand out, as we propose a data efficient method for task unlearning:
the main theme - Efficiency in Model Algorithms, Training, and Inference (speaks for itself, but there is a summary on the website)
Low-resource Methods for NLP
Ethics, Bias, and Fairness (potentially, since harmfulness unlearning is a part of it)
Information Extraction (in our case task efficiency decrease/removal)
Question Answering (one of the tasks unlearned - harmful question answering)
Two stage review: 1st submit to ARR, then commit to EMNLP 2024 - they make the final decision. Commitment deadline: 20th August 2024.
Other important dates:
Notification of acceptance: 20th September 2024
Camera-ready papers due: 3rd October 2024
Main Conference 12-14th November 2024
Workshops + Tutorials: 15th-16th November 2024
To decide: Long (8 pages) vs short (4 pages) paper: https://aclrollingreview.org/cfp#long-papers (probably long, as we want to include quite a bit of concrete evaluation and analysis)
Two-Way anonymised review: can't reveal names or affiliation in submission (including self references "X showed in .." vs "We previously showed in.."). Supplementary material (read: GH repo) should also be anonymised: https://anonymous.4open.science/
Ethics policy: authors encouraged to include ethical impact of the work (however not required), will be taken into account in the review process https://aclrollingreview.org/cfp#ethics-policy
AI writing assistance: disclose if used a lot, disclose in README if used for code writing assitance
FINAL VERSION: given 1 additional page of content, to address reviewers' comments (9 pages for long total), assuming that means we submit with 8, only expand to 9 to address comments
PResentation: if accepted ,someone need s to present in person or virtually otherwise it will be rejected
Enjoy the tl;dr, I tried to capture it all (handwritten), but defo worth another glance @Adamliu1 @Davidyz @TheRootOf3
Migrated from google doc