Instructions for Reviewers
Thank you for reviewing for EMNLP 2020! In order to ensure the quality of reviews, we would like to share with you the following instructions for reviewing EMNLP 2020 papers. Please read these instructions before you start reviewing papers.
Please note that the content of any submission to EMNLP 2020, and the participants in and content of discussion on submissions, are confidential.
1. In-Depth Review: This section is for you to give your overall assessment of the paper and to provide evidence to support your opinions. There are 7 subsections:
- The core review: This is the most important part. It should include your view of the main contributions that the paper intended to make and how well it succeeds at making these contributions. From your point of view, what are the significant strong and weak parts of the paper and the work it describes? This could be a 2 paragraph (or longer) essay and/or bullet points. Remember to describe how the work advances the state of knowledge in computational linguistics and/or highlights why it fails to make a sufficient contribution.
- Reasons to accept: please briefly summarize from your core review the main reasons why this paper should be accepted for the conference, and how the NLP community would benefit from it. You may refer back to your review to provide more context and details.
- Reasons to reject: please briefly summarize the main reasons that this paper cannot be published and presented in its current form. What are the parts that would need to be improved in order to advance the state of knowledge?
- Reproducibility: This year we introduced a reproducibility checklist in an effort to increase reproducibility of the research work in NLP (see EMNLP 2020 call for papers and reproducibility post). In the review form, please answer the following two questions.
- "How do you rate the paper's reproducibility? Will members of the ACL community be able to reproduce or verify the results in this paper?" Scores of 1-5 are used to assess this aspect. The detailed explanation for each point level is provided in the review form. N/A may be used for papers that do not include empirical results.
- "Are the authors' answers to the Reproducibility Checklist useful for evaluating the submission?". Three choices are provided for this question (very useful, somewhat useful, not useful). Note that this question is for us to collect feedback regarding the usefulness of the reproducibility checklist, and is not about evaluating the paper itself.
Overall recommendation: Here you are asked to synthesize the above and come up with your own recommendation for the paper.
- Like ACL2020, we have used a 5 point scale with a half point increments. The detailed explanation for each point level is provided in the review form. These numbers are just a concise way of expressing your overall opinion and relative importance of the factors mentioned above.
- Different from ACL 2020, we are allowing a rating of 3 (ambivalent). Please try to take a stand on whether the paper is above or below the borderline, e.g., by selecting 2.5 or 3.5. However, as much as we would like you to do that, if you think this is indeed a borderline paper or you are not able to decide, you should use 3.
- Decisions will be made not just on the scores and certainly not on average scores, but will also take into account the whole review, reviewer discussion and Area Chair meta-reviews and recommendations. However it is important to align your recommendation with the reasoning given above, so that authors will be able to understand the motivation for the recommendations and how decisions were arrived at.
- Reviewer confidence: This section should be used to inform the committee and authors how confident you are about your recommendation, taking into account your own expertise and familiarity with this area and the paper's contents.
- Author response: There will be an author response period. It is important for you to check whether author responses have cleared up your questions or misunderstandings. This may influence your overall recommendation and the core review. If that's the case, please update your recommendation and review accordingly (and state in your review any new decisions you made so the Area Chairs are aware).
2. Questions and Additional Feedback for the Authors: Since we will have an author response process, for questions you would like the author(s) to respond to during the response period, please include them here. This is also the place for you to give suggestions to the authors to help them improve the paper for the final version (or a future submission).
3. Confidential Information: Your answers to questions in this section will not be shared with the authors. Here we ask you about recommendations for awards, any ethical concerns, and confidential comments to the area chairs and/or PC chairs.
For Best Paper Award, please be open minded and feel free to nominate good quality papers even though they may not be the typical types. These can be a survey paper, an opinion paper, a paper about resources and datasets, a paper for low resource language, an analysis paper, etc. A committee will evaluate best paper candidates, and we would like to have a wide variety of paper types in the candidate pool, not just vanilla empirical research papers. In addition to the best long paper award and best short paper award, we will give several outstanding paper awards.
Supplementary materials are allowed as a stand-alone document uploaded as an additional file. Supplementary materials are, as the name suggests, supplementary, and you have no obligation to read them. You should treat them like other citations in submissions that may be helpful in understanding background or details beyond the scope of the paper itself.
However, as noted above, given the new requirement for reproducibility, authors may provide additional information about their datasets and experiments in Appendix, and attach a zip file with resources such as code and data. Please take some time to check those, if applicable. Uploading data and/or code alongside paper submissions is preferred over supplying a hyperlink. The latter could violate double-blind review practices.
As in most previous NLP conferences, you are allowed to solicit help from others. However, when it comes to writing the final review and giving the final scores, we expect you to take the secondary reviewer's review and rewrite it using your own words and adjust the scores when you see fit. Essentially, the final review should reflect your own opinions about the paper, and you need to be able to justify the opinions you present in the final review.
The program chairs and area chairs have already identified submissions that violated our formatting guidelines and have desk-rejected those submissions. Therefore, you do not need to worry about formatting issues with the submissions assigned to you. If you think the paper violates the format guidelines, please contact your area chairs or PCs. Otherwise, the paper format shouldn't be used to down weight evaluation of the paper.
Your reviews are due Thursday, July 30, 2020 (11:59pm anywhere on Earth). Please note that there is a reviewer discussion period from August 14 to 24 after the author response. Your duties are listed below. Don't leave reviewing to the last minute!
- June 29 – July 30: Review Period
- August 7 – 13: Author response period
- August 14 – 20: Reviewer discussion period
Please read our post on writing good reviews. That post will be publicized before the author rebuttal period such that the authors are aware of the guidelines and can reference them in their rebuttal. ACs will be instructed to flag poor reviews, ask reviewers to revise their reviews or provide objective reasons to justify your positions.
We are introducing a new acceptance option for papers that narrowly miss out on publication in EMNLP, but are judged to be worthy of publication, to be published in a special new publication venue, dubbed Findings of EMNLP. This will affect the acceptance decisions made by ACs, SACs and the PCs, but will not require any specific inputs from reviewers.