Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› international-natural-language-generation-conference
international-natural-language-generation-conference
GitHub针对INLG会议的投稿策略工具，评估稿件契合度、重构叙事框架、检查证据缺口及匿名规范。适用于确定目标为INLG或需将论文调整为符合该顶会标准的场景，辅助作者规避拒稿风险并优化提交质量。
Trigger Scenarios
作者指定INLG为目标投稿 venue
需对自然语言生成类手稿进行会议适配性评估
需将期刊或arXiv风格论文重构为CS会议叙事
需要诊断证据缺口、匿名合规性或反 rebuttal 策略
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill international-natural-language-generation-conference -g -y
SKILL.md
Frontmatter
{
"name": "international-natural-language-generation-conference",
"description": "Use when targeting International Natural Language Generation Conference (INLG) 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 generation."
}
International Natural Language Generation Conference (INLG)
Conference positioning
International Natural Language Generation Conference (INLG) is a top computer-science conference venue for natural language generation, evaluation, controllability, data-to-text, dialogue, and LLM generation. It rewards a generation paper with rigorous human or automatic evaluation and clear generation task framing. 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 INLG / International Natural Language Generation Conference as the target venue.
- A manuscript in natural language generation 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: natural language generation, evaluation, controllability, data-to-text, dialogue, and LLM generation.
- 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 INLG'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 INLG as a language generation 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: natural language generation, evaluation, controllability, data-to-text, dialogue, and LLM generation.
- Distinctive fingerprint for reviewer calibration: natural, language, generation, evaluation, controllability, data-to-text, dialogue, venue-specific, contribution, inlgmeeting, github.
- Official anchor domain: inlgmeeting.github.io. 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 generation and the author can say why INLG reviewers are the primary audience, not merely a convenient deadline.
- Closest roster neighbors to compare before final routing:
north-american-chapter-of-the- association-for-computational-linguistics(NAACL),european-chapter-of-the-association- for-computational-linguistics(EACL),sigdial-conference-on-discourse-and-dialogue(SIGDIAL),joint-international-conference-on-computational-linguistics-language-resources- and-evaluation(LREC-COLING). Break ties by contribution type, evidence shape, reviewer community, and the current official CFP from inlgmeeting.github.io.
What distinguishes this venue from its closest siblings
- What INLG is. The International Natural Language Generation Conference (ACL SIGGEN) — natural language generation specifically: data-to-text, surface realization, planning, and generation evaluation.
- *vs SEM. *SEM is about semantics/meaning representation, a different SIG community; route generation work here, meaning-analysis work there.
- vs ACL/EMNLP. The big ACL-family meetings absorb generation too; pick INLG when the NLG community is the primary audience.
INLG-specific routing detail
- Prefer INLG when the paper studies text generation itself: planning, realization, controllability, data-to-text, generation evaluation, factuality, style, or human assessment of generated language.
- Route meaning representation, semantic parsing, lexical semantics, and entailment analysis to *SEM unless the main contribution is producing language.
- INLG evidence should make the generation task, evaluation protocol, human-rating reliability, prompt/model controls, and example-to-metric connection explicit.
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 INLG, the evidence must support the venue-specific signature: a generation paper with rigorous human or automatic evaluation and clear generation task framing.
- 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://inlgmeeting.github.io/
- 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 INLG, 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 generation 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 INLG reviewers too little technical or empirical substance.
Re-routing decision
If the paper misses INLG'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
[Fit] High / Medium / Low (one-line reason)
[Target] International Natural Language Generation Conference (INLG)
[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>
Version History
- 1839142 Current 2026-07-05 12:45
