--- name: north-american-chapter-of-the-association-for-computational-linguistics description: Use when targeting Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for NLP regional flagship. --- # Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) ## Conference positioning Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) is a top computer-science conference venue for NLP and computational linguistics with North American ACL community focus. It rewards a language paper with ACL-level rigor and fit for the NAACL cycle or region-specific audience. Treat this skill as a **fit / venue-selection / re-framing** tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal. Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in `../../resources/conference-roster.md` and `../../resources/official-source-map.md`. ## When to trigger - The author names NAACL / Annual Conference of the North American Chapter of the Association for Computational Linguistics as the target venue. - A manuscript in NLP and computational linguistics with North American ACL community focus needs a conference-fit read before being formatted or submitted. - The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative. - The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue. ## Scope & topic fit - Core fit: NLP and computational linguistics with North American ACL community focus. - Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis. - The paper should explain why the result matters to NAACL's reviewers, not just why it is interesting to the authors' lab or product context. - Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness. - If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence. ## Venue-specific calibration - Reviewer lens: Treat NAACL as an ACL-family venue with a North American chapter identity and a full NLP breadth. The paper should still satisfy ACL-level modeling, evaluation, and linguistic rigor; the regional chapter is not a lower evidence bar. - Contribution hook to foreground: the venue-specific contribution bar. - Scope vocabulary to use naturally in the abstract and introduction: NLP and computational linguistics with North American ACL community focus. - Distinctive fingerprint for reviewer calibration: computational, linguistics, north, american, community, focus, venue-specific, contribution, regional, flagship, naacl. - Official anchor domain: naacl.org. Quote annual rules only after opening that source and the current-year CFP/author kit. ## Method & evidence bar - Use task-appropriate baselines, multiple datasets or languages when the claim is broad, and error analysis that explains model behavior. - For LLM work, control for data leakage, prompt sensitivity, evaluation contamination, and human-evaluation reliability. - For resources, document annotation, licensing, demographics, quality control, and intended use. - For NAACL, the evidence must support the venue-specific signature: a language paper with ACL-level rigor and fit for the NAACL cycle or region-specific audience. - Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim. ## Structure & house style - State the language phenomenon, task, or system behavior before the model name. - Connect examples to measured errors; reviewers dislike anecdotal examples presented as evidence. - Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles. - The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim. - Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution. ## Official-cycle checklist - Open the live official venue page: https://naacl.org/ - Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements. - Confirm the review workflow and portal: ARR/START/ACL Rolling Review or the current ACL-family submission portal, plus ACLPUB formatting when applicable. - Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms. - If the live official instructions conflict with this skill, the official instructions win. ## Pre-submission self-check - [ ] One sentence states why this manuscript belongs at NAACL, using the venue's scope rather than generic "top conference" language. - [ ] The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support. - [ ] Related work includes the nearest current-cycle NLP regional flagship papers and explains the technical delta. - [ ] The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements. - [ ] The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links. ## Common desk-reject triggers - Evaluation that is only a prompt table or cherry-picked generation examples. - Missing dataset documentation, licensing, or annotation reliability. - Claims of general language understanding from narrow English-only benchmarks. - Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy. - A contribution framed for a neighboring field while giving NAACL reviewers too little technical or empirical substance. ## Re-routing decision If the paper misses NAACL's bar, compare against `annual-meeting-of-the-association-for-computational-linguistics` / `conference-on-empirical-methods-in-natural-language-processing` / `european-chapter-of-the-association-for-computational-linguistics` / `interspeech`. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them. ## Output format ```text [Fit] High / Medium / Low (one-line reason) [Target] Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) [Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other [Main evidence gap] [Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready [Top rejection risk] [Re-route suggestion] ```