EMNLP 2025 Decisions: Community Insights and the Controversial Desk Rejections

[Sylvia]

The final decisions for EMNLP 2025 have been released, sparking a wave of reactions across research communities on social media such as Zhihu and Reddit. Beyond the excitement of acceptances and the disappointment of rejections, this cycle is marked by a remarkable policy twist: 82 papers were desk-rejected because at least one author had been identified as an irresponsible reviewer. This article provides an in-depth look at the decision process, the broader community responses, and a comprehensive table of decision outcomes shared publicly by researchers.

Key Announcements from the Decision Letter

The program chairs’ decision email highlighted several important points:

1. Acceptance Statistics

   • 8174 submissions received.
   • 22.16% accepted to the Main Conference.
   • 17.35% accepted as Findings.
   • 82 papers desk-rejected due to irresponsible reviewer identification.

2. Desk Rejections Linked to Reviewer Misconduct

   • A novel and controversial policy: authors who were flagged as irresponsible reviewers had their own papers automatically desk-rejected.
   • The official blog post elaborates on what qualifies as irresponsible reviewing (e.g., extremely short, low-quality, or AI-generated reviews).

3. Camera-Ready Submissions

   • Deadline: September 19, 2025.
   • Authors must fill in the Responsible NLP checklist, which will be published in the ACL Anthology alongside the paper.
   • Allowed: one extra page for content, one page for limitations (mandatory), optional ethics, unlimited references.

4. Presentation and Logistics

   • Papers must be presented either in person or virtually to be included in proceedings.
   • Oral vs. poster presentation decisions will be finalized after camera-ready submission.
   • Registration deadline: October 3 (at least one author), with early in-person registration by October 6 due to Chinese government approval processes (conference will be in Suzhou).

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The Desk Rejection Controversy: 82 Papers Removed

This year’s 82 desk rejections triggered heated debates. While ensuring reviewer accountability is laudable, punishing co-authors for the actions of a single irresponsible reviewer is unprecedented and raises questions about fairness:

• Collective punishment? Innocent co-authors had their work invalidated.
• Transparency gap: The official blog post provided criteria, but the actual identification process is opaque.
• Potential chilling effect: Researchers may hesitate to serve as reviewers for fear of inadvertently harming their own submissions.

The policy signals a stronger stance by ACL conferences toward review quality enforcement, but it also underscores the urgent need for more transparent, community-driven reviewer accountability mechanisms.

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Community Voices: Decisions Shared by Researchers

To capture the breadth of community sentiment, below is a comprehensive table compiling decision outcomes (OA = overall average reviewer scores, Meta = meta-review score) shared publicly across Zhihu, Reddit and X.

This table is exhaustive with respect to all shared samples from the provided community discussions.

OA Scores (per reviewer)	Meta	Outcome	Track / Notes / User
4, 4, 3	4	Main	Meta reviewer wrote a detailed essay, helped acceptance
3.5, 3.5, 2	—	Main	Initially worried, accepted to main
2.67 (avg)	3.5	Main	Shared proudly (“unexpected”)
3.67	4	Main	Confirmed traveling to Suzhou
3.33 (4, 3.5, 2.5)	3	Rejected	Author frustrated, “don’t understand decision”
3.0	3	Rejected	Hoped for Findings, didn’t get in
3.0	3.5	Main (short)	Track: multilinguality & language diversity; first-author undergrad
2.33	3.5	Findings	Efficient NLP track
3.33	3.5	Main	Efficient NLP track
3.5, 3.5, 2.5	2.5	Findings	Meta review accused of copy-paste from weakest reviewer
3, 3.5, 4	3	Main	Theme track
4, 3, 2	2.5	Rejected	One review flagged as AI-generated; rebuttal ignored
4.5, 2.5, 2	—	Rejected	Meta only two sentences
3.38	3.5	Main	Rejected at ACL before; accepted at EMNLP
2, 3, 3	3	Rejected	RepresentativeBed838
3.5, 3, 2.5	3.5	Rejected	Author shocked
3, 3, 3	3	Rejected	Multiple confirmations
5, 4, 3.5	4.5	Main	Track: Dialogue and Interactive Systems
3.5, 4.5, 4	4	Main	GlitteringEnd5311
3, 3.5, 3.5	3.5	Main	Retrieval-Augmented LM track
2.5, 3, 3	3	Findings	After rebuttal challenge; author reported meta reviewer
1.5, 3, 3 → rebuttal → 2.5, 3, 3.5	3.5	Main	Initially borderline, improved after rebuttal
3.67	3	Main	Computational Social Science / NLP for Social Good track
4, 3, 3	3	Main	Low-resource track
3.5, 3.5, 3	3.5	Main	Low-resource track
4, 3	3	Findings	Author sad (“wish it was main”)
Overall 3.17	3	Findings	JasraTheBland confirmation
Overall 3.17	3.5	Main	AI Agents track
Overall 3.17	3	Findings	AI Agents track
4, 3, 2	3.5	Main	Responsible-Pie-5882
3.5 (avg)	3.5	Main	Few_Refrigerator8308
3, 3, 3.5 → rebuttal → 3.5,3.5,3.5	4.0	Main	LLM Efficiency track
3.5, 2.5, 2.5	3	Findings	FoxSuspicious7521
3, 3.5, 3.5	3.5	Main	ConcernConscious4131 (paper 1)
2, 3, 3.5	3	Rejected	ConcernConscious4131 (paper 2)
3, 3, 3	3	Rejected	Ok-Dot125 confirmation
3.17 (approx)	3.5	Main	Old_Toe_6707 in AI Agents
3.17 (approx)	3	Findings	Slight_Armadillo_552 in AI Agents
3, 3, 3	3	Rejected	Confirmed again by AdministrativeRub484
4, 3, 2	3.5	Main	Responsible-Pie-5882 (duplicate entry but reconfirmed)
3.5, 3.5, 3	3.5	Main	breadwineandtits
3, 3, 3	3	Accepted (Findings or Main unclear)	NeuralNet7 (saw camera-ready enabled)
2.5 (meta only)	2.5	Findings	Mentioned as borderline acceptance
3.0	3.0	Findings	shahroz01, expected
4, 3, 2	3.5	Main	Responsible-Pie-5882 (explicit post)
3.5, 3.5, 2.5	2.5	Findings	Practical_Pomelo_636
3, 3, 3	3	Reject	Multiple confirmations across threads
4, 3, 3	3	Findings	LastRepair2290 (sad it wasn’t main)
3.5, 3, 2.5	3.5	Rejected	Aromatic-Clue-2720
3, 3, 3.5	3.5	Main	ConcernConscious4131
2, 3, 3	3	Reject	ConcernConscious4131
3, 3, 3	3	Reject	Ok-Dot125 again
3.5, 3.5, 3	3.5	Main	Few_Refrigerator8308 second report
3.5, 3, 2.5	3.5	Rejected	Aromatic-Clue-2720
4, 3, 2	3.5	Main	Responsible-Pie-5882 final confirmation
3.5, 3.5, 3	3.5	Main	Reconfirmed across threads
3, 3, 3	3	Rejected	Reported multiple times
2.5 (OA overall)	3.0	Findings	Outrageous-Lake-5569 reference

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Patterns Emerging

From the collected outcomes, some patterns can be observed:

• Meta ≥ 3.5 often leads to Main acceptance (even when individual OA scores are mediocre, e.g., 2.67).
• Meta = 3 cases are unstable: some lead to Findings, others to Rejection, and in a few cases even Main.
• Meta < 3 almost always means rejection, with rare exceptions.
• Reviewer quality matters: multiple complaints mention meta-reviews simply copy-pasting from the weakest reviewer, undermining rebuttals.

This highlights the high variance in borderline cases and explains why so many authors felt frustrated or confused.

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Conclusion: Lessons from EMNLP 2025

EMNLP 2025 brought both joy and heartbreak. With a Main acceptance rate of just over 22%, competition was fierce. The desk rejections tied to reviewer misconduct added an entirely new layer of controversy that will likely remain debated long after the conference.

For researchers, the key takeaways are:

• Meta review scores dominate: cultivate strong rebuttals and area chair engagement.
• Borderline cases are unpredictable: even a 3.5 meta may result in Findings instead of Main.
• Reviewer accountability is a double-edged sword: while improving review quality is necessary, policies that punish co-authors risk alienating the community.

As the field grows, the CL community must balance fairness, rigor, and transparency—a challenge as difficult as the NLP problems we study.