eacl-writing-style

name	eacl-writing-style
description	Use when revising an EACL paper's prose for a task-first first page, a concrete page-one example, scoped LLM-era claims, quantified error analysis, an anonymity-safe voice, honest Limitations, and compression into the 8-page long or 4-page short content budget without pushing the argument into appendices.

EACL Writing Style

Use this to revise an EACL paper so its contribution is legible fast and its claims are scoped. EACL reviewers read many papers in a short window; the ones that land put the task and the result on the first page, quantify rather than assert, and name their limits. Pair this with the worked example in ../../resources/worked-examples/01-introduction.md.

The EACL first-page arc

1. Task — the specific problem, in the first breath, not "great progress in NLP."
2. Gap — why current methods fall short, each reason nameable.
3. What we do — the contribution, stated plainly.
4. Measured result — a number tied to a table, with variance.
5. Honest scope — what the result does and does not cover.

Habits to cut, habits to keep

Cut	Keep
"Achieves strong performance"	"Improves F1 by X (95% CI ...) over baseline B"
Generic "prior work is limited"	A specific failure per cited approach
A concrete example only on page 5	A worked example on page 1
Unscoped "our method generalizes"	"On the six languages tested; see Limitations"
Roadmap standing in for an argument	A one-line roadmap after the argument

Scope the LLM-era claim

• If the paper uses or evaluates LLMs, bound the claim to the models, prompts, and settings tested, and disclose contamination risk. An unscoped "LLMs can/cannot do X" invites the reviewer to name the counterexample.
• Report prompts and decoding as part of the method, not as trivia (see eacl-reproducibility).

Quantify the error analysis

• A page-one or early-section error analysis with counts ("40% of errors are agreement errors; examples in Table 3") is worth more than adjectives. EACL rewards papers that show where and why a system fails, especially across languages.

Anonymity-safe voice

Anonymity check before submission:
  - no author names, affiliations, or acknowledgements
  - no "as we showed in our EMNLP 2025 paper" -> use third-person citation
  - no links that identify authors (personal repos, named grant pages)
  - self-citations phrased neutrally

Multilingual clarity

• Name languages and scripts explicitly; render diacritics correctly in the PDF and later in the Anthology metadata (eacl-camera-ready).
• Do not let an aggregate multilingual score stand in for per-language honesty — a table beats an average.

Compression discipline

• The content pages carry the argument; appendices carry detail. If cutting for length pushes a core claim into an appendix, cut something else instead (see eacl-supplementary).
• The Limitations section is free space and read — use it to state scope, not to hide results.

Output format

[First-page arc] Present / Missing elements: <task/gap/what/result/scope>
[Overclaims] <phrases to scope, with fix>
[Evidence pairing] <claims lacking a table/number>
[Anonymity] <any leak>
[Multilingual honesty] <aggregate-hiding issues>
[Compression] <what to cut so the body carries the claim>