Joint International Conference On Computational Linguistics Language R

---
name: joint-international-conference-on-computational-linguistics-lang
description: "Use when targeting Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING) 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 language resources."
category: data-science-and-ml
source_repo: brycewang-stanford/Awesome-Journal-Skills
source_path: "Computer-Science-Conference-Skills/skills/joint-international-conference-on-computational-linguistics-language-resources-and-evaluation/SKILL.md"
source_url: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Computer-Science-Conference-Skills/skills/joint-international-conference-on-computational-linguistics-language-resources-and-evaluation/SKILL.md
---


# Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)

## Conference positioning

Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING) is a top computer-science conference venue for language resources, evaluation, corpora, annotation, linguistic infrastructure, and computational linguistics. It rewards a resource or evaluation paper with documentation, licensing, annotation reliability, and reuse value. 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 LREC-COLING / Joint International Conference on Computational Linguistics, Language Resources and Evaluation as the target venue.
- A manuscript in language resources 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: language resources, evaluation, corpora, annotation, linguistic infrastructure, and computational linguistics.
- 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 LREC-COLING'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 LREC-COLING as a language resources venue whose reviewers expect the scope and evidence to match its own community. Do not submit a generic CS paper until the introduction names the exact subcommunity, contribution type, and proof or empirical standard.
- Contribution hook to foreground: the venue-specific contribution bar.
- Scope vocabulary to use naturally in the abstract and introduction: language resources, evaluation, corpora, annotation, linguistic infrastructure, and computational linguistics.
- Distinctive fingerprint for reviewer calibration: language, resources, evaluation, corpora, annotation, linguistic, infrastructure, computational, linguistics, venue-specific, contribution, lrec-coling.
- Official anchor domain: lrec-coling.org. Quote annual rules only after opening that source and the current-year CFP/author kit.

## Close-neighbor routing guardrail

- Use this profile only when the manuscript's central contribution is genuinely in language
  resources and the author can say why LREC-COLING reviewers are the primary audience, not
  merely a convenient deadline.
- Closest roster neighbors to compare before final routing: `international-natural-language-
  generation-conference` (INLG), `sigdial-conference-on-discourse-and-dialogue` (SIGDIAL),
  `starsem-conference-on-computational-semantics` (*SEM), `interspeech` (INTERSPEECH). Break
  ties by contribution type, evidence shape, reviewer community, and the current official CFP
  from lrec-coling.org.

## LREC-COLING-specific routing detail

- Prefer LREC-COLING when the contribution is computational linguistics, language resources, annotation, evaluation, corpora, multilingual data, or linguistically grounded NLP methods.
- Route programming-language systems to OOPSLA/PLDI/ICFP, generation-specific papers to INLG, and broad NLP empirical work to ACL/EMNLP/NAACL when resource/evaluation focus is not central.
- LREC-COLING evidence should include data provenance, annotation protocol, licensing, linguistic coverage, evaluation design, and intended resource use.

## 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 LREC-COLING, the evidence must support the venue-specific signature: a resource or evaluation paper with documentation, licensing, annotation reliability, and reuse value.
- 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://lrec-coling.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 LREC-COLING, 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 language resources 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 LREC-COLING reviewers too little technical or empirical substance.

## Re-routing decision

If the paper misses LREC-COLING's bar, compare against `annual-meeting-of-the-association-for-computational-linguistics` / `conference-on-empirical-methods-in-natural-language-processing` / `north-american-chapter-of-the-association-for-computational-linguistics` / `european-chapter-of-the-association-for-computational-linguistics`. 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] Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>
```

---

**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Computer-Science-Conference-Skills/skills/joint-international-conference-on-computational-linguistics-language-resources-and-evaluation/SKILL.md`