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ACL ARR 2026年1月元評論

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🤖閱讀原文: Reddit r/MachineLearning

💡Real ACL ARR scores guide submission decisions for 2026 cycle.

⚡ 30-Second TL;DR

有什麼變化

獲得分數:4.5(conf 5)、3.5(conf 3)、3(conf 3)

為什麼重要

一位研究者分享其首篇ACL ARR一月週期的投稿分數:在回應審稿意見後獲得4.5(conf 5)、3.5(conf 3)、3(conf 3)。他們等待3月10日的元評論,以決定是否承諾提交ACL 2026主會或Findings軌道。

下一步行動

Compare your ACL ARR scores to historical thresholds before committing.

誰應關注:Researchers & Academics

關鍵要點

  • 獲得分數:4.5(conf 5)、3.5(conf 3)、3(conf 3)
  • 元評論定於3月10日發布
  • 考慮承諾提交ACL 2026主會或Findings

🧠 深度解析

背景與延伸:來自公開資料,非原文內容。引用 5 個來源。

🔑 增強重點摘要

  • ACL ARR (Anthology Rolling Review) is a continuous review process for ACL submissions, allowing authors to submit anytime and commit reviews to conferences like ACL 2026 main or Findings track after meta-reviews.
  • The scores (4.5 with confidence 5, 3.5 conf 3, 3 conf 3) reflect reviewer assessments on criteria like soundness and excitement; borderline scores like these often lead to debates on committing to main conference versus Findings.
  • Meta-reviews for ACL ARR January 2026 cycle are scheduled for March 10, 2026, synthesizing reviewer scores to guide commitment decisions.
  • ACL 2026 main conference is set for July 2-7, 2026 in San Diego, CA, with related events like Student Research Workshop (SRW) having ARR commitment deadline on April 15, 2026.
  • Workshops like TrustNLP accept fast-track submissions from ACL ARR/EMNLP if average rating exceeds 2.75, providing alternatives for non-main track papers.

時間線

2025-12-11
ACL 2026 SRW website launched.
2025-12-23
ACL 2026 SRW Call for Papers released.
2026-02-04
ACL 2026 SRW pre-submission mentorship application deadline.
2026-02-13
ACL 2026 SRW pre-submission mentorship deadline passed, 113 submissions received; SRW site last updated.

📎 來源 (5)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

  1. acl2026-srw.github.io
  2. trustnlpworkshop.github.io
  3. computer.org — Calls for Papers
  4. illc.uva.nl — Cls
  5. arXiv — Recent
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原始來源: Reddit r/MachineLearning

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