EMNLP Artifact Evaluation

Use this for evidence packaging around an EMNLP submission. EMNLP has no separate artifact-badging committee; the artifact is part of the scientific claim, filed under the Responsible NLP checklist and inspected at reviewer discretion. In NLP the artifact surface is unusually broad — data, labels, prompts, outputs, and scoring code are all first-class — and each has its own failure mode.

What NLP reviewers open, in order

Reviewers with thirty minutes and suspicion follow a predictable path:

Package for this order: a top-level README that routes to each artifact in one hop, a data sample small enough to open in a text editor, and prompts stored as files rather than embedded in code.

Dataset packaging: the data statement standard

A released corpus travels with documentation or it travels badly:

• Provenance — where text came from, collection dates, and the license chain that permits redistribution (scraped ≠ redistributable; say what you verified).
• Annotation record — who labeled (recruitment, qualifications, compensation), under what guidelines (included verbatim), with what agreement (statistic and value), and how disagreements were resolved.
• Composition — languages, domains, demographic scope of speakers and annotators where relevant; the boundaries here feed the Limitations section directly.
• Intended use — the task the labels are valid for, and foreseeable misuses; a toxicity corpus without a misuse note reads as naive in 2026.
• Splits and hygiene — split construction, dedup across splits, and any overlap audit against common pretraining corpora.

Prompts and outputs as archival objects

Treat prompts like code and outputs like data:

artifacts/
├── prompts/
│   ├── task_nli_zeroshot_v3.txt      # verbatim, one file per reported condition
│   └── CHANGELOG.md                  # v1→v3: what changed and which tables use which
├── outputs/
│   ├── model=llama3-8b_seed=42.jsonl # every generation behind every reported number
│   └── sample_100_stratified.jsonl   # reviewer-sized random sample, stratified by error class
└── scoring/
    └── score.py                      # reads outputs/, emits the paper's tables

The outputs/ directory is the underrated one: releasing full generations lets a reviewer (and later, the field) re-score with better metrics without rerunning models — the single highest-leverage reproducibility gift an NLP paper can give.

Review-time anonymity vs release-time findability

The same package lives two lives. At review: anonymized hosting, no usernames in paths, no lab-identifying data sources, license files present but grant numbers absent. After acceptance: permanent home, citable version (tag or DOI), README pointing at the Anthology entry, model cards / data statements filled with the identity-bearing detail review forbade. Build the package once with an anonymize.sh that strips the delta, rather than maintaining two diverging trees.

Licensing without a legal department

The checklist asks about licenses in both directions — what you used and what you release — and most failures are ignorance, not malice:

• Inbound: a benchmark's license travels with its derivatives; a corpus built by filtering a research-only dataset cannot ship under a commercial-friendly license. Record the license of every ingredient at collection time, because reconstructing the chain in deadline week fails.
• Model outputs: generations from API models may carry terms-of-use restrictions on redistribution and on training-data use; releasing an output corpus without checking the provider's terms is the current decade's version of scraping-and- hoping.
• Outbound: pick a standard license (CC for data, a common OSS license for code), state it in the README and in the paper, and resist inventing bespoke "research only, email us" terms that make the artifact uncitable in practice.
• People in the data: licensing does not settle privacy; text containing personal information needs handling and redaction notes independent of copyright status.

Restricted and unreleasable material

When data cannot ship (privacy, license, platform terms): release the collection pipeline and filters instead of the corpus; provide a synthetic or public-subset sample demonstrating the format; state retention terms and whom to contact for research access. For closed API models, ship exact prompts, dates, decoding parameters, and full outputs — the model is closed; your measurement of it need not be. The checklist rewards documented honesty over silent omission in every one of these cases.

Versioning after release

An NLP artifact that succeeds gets used, and use creates obligations the paper never mentioned:

• Tag the exact version the camera-ready numbers came from and never move that tag; fixes go in new versions with a changelog explaining what results they might shift.
• When label errors are found post-release (they will be), publish errata in the repository rather than silently rewriting files other papers already evaluated on — benchmark drift breaks comparability for everyone downstream.
• Keep an issue tracker open; the questions users file are a free audit of your documentation's gaps and occasionally of your paper's claims.

Output format

[Package role] review-time evidence / camera-ready release / both
[Inspection path] <README -> data sample -> prompts -> scoring -> outputs: intact?>
[Data statement] <provenance / annotation / composition / use / splits — gaps>
[Anonymity delta] <what anonymize.sh strips; leaks found>
[Unreleasable items] <artifact -> documented workaround>