# Acl Topic Selection

Use when deciding whether a project fits ACL versus EMNLP, NAACL, EACL, TACL...

- Repository: https://github.com/brycewang-stanford/Awesome-Journal-Skills (1346 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-09-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-topic-selection -g -a claude-code -y`
Codex: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-topic-selection -g -a codex -y`
Cursor: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-topic-selection -g -a cursor -y`
Gemini CLI: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-topic-selection -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Awesome-Journal-Skills`

## Description

Use when deciding whether a project fits ACL versus EMNLP, NAACL, EACL, TACL, Computational Linguistics, COLM, or an ML venue, covering contribution typing for NLP work, long-versus-short paper choice, the annual theme track, Findings-tier expectations, and sharpening the computational-linguistics framing before writing starts.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# ACL Topic Selection

Use this before the first draft. ACL is the flagship of the \*ACL family:
broadest scope across computational linguistics and NLP, the most competitive
main-program bar, and, under ACL Rolling Review, a venue choice you finalize
at *commitment* time, which gives topic strategy an unusual second chance.

## What ACL rewards

- A contribution about **language**: modeling it, measuring it, resourcing it,
  or explaining how systems process it, with the linguistic question visible,
  not incidental.
- Typed contributions reviewers can classify fast: method, resource,
  evaluation/metric, analysis, theory, or position. Papers that are half
  method and half unvalidated resource read as neither.
- Evidence proportional to breadth (see `acl-experiments`) and an error
  analysis that says something about language, not just scores.
- Work engaging the current field conversation, for ACL 2026, the special
  theme was explainability of NLP models, with a dedicated Thematic Paper
  Award; each edition names its own theme.

## Family routing

| Signal | Better home |
|---|---|
| Core NLP contribution, broad audience, strongest possible reviews wanted | ACL (or whichever \*ACL your ARR package is eligible to commit to) |
| Empirical, engineering-forward NLP; dense experimental papers | EMNLP, historically the empirical sibling, same ARR pipeline |
| Regional relevance, or timing fits its cycle windows | NAACL / EACL / AACL |
| Needs >9 pages, revision-based journal reviewing, no conference clock | TACL (journal, also Anthology-published) |
| Survey-scale or theoretical linguistics depth | Computational Linguistics (journal) |
| LLM-centric work thin on language questions | COLM or an ML venue (NeurIPS/ICML/ICLR) |
| Deployed-system lessons, product constraints | ACL industry track, separate CFP and deadlines |
| Early-stage, student-led | ACL Student Research Workshop |

The rest: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/ACL-Skills/skills/acl-topic-selection/SKILL.md