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 |
Because commitment is decoupled, "ACL vs EMNLP" is often not a submission-time
decision: submit to ARR when ready, then commit to the conference whose window
and bar the finished package fits.
Long or short
- Long (8 pages): a complete arc — method or resource, evaluation, analysis.
- Short (4 pages): one falsifiable point with one decisive experiment; a
negative result, a focused analysis, an evaluation flaw demonstrated.
Short papers are judged as short papers — reviewers reject compressed long
papers but reward genuinely small, sharp claims.
Fit sharpening before writing
- Write the one-sentence claim naming the linguistic object: task,
phenomenon, language set, or evaluation practice.
- Name the reviewer community: who at ACL wants this answer? If the honest
answer is "ML engineers," reconsider the venue or reframe toward the
language question.
- Check the theme track: a solid paper matching the year's theme gains a
natural reviewer pool and an award lane.
- Stress-test the Findings scenario: would a Findings acceptance satisfy
the project's goals? If not, ask what would push it into the main program
— usually analysis depth or evaluation breadth — and plan that now.
- Verify novelty against the last two *ACL rounds specifically
(see
acl-related-work); ACL's most common fit failure is a project
scooped between conception and cycle deadline.
Vignette: routing an LLM evaluation project
A team measures whether chat models track discourse referents across long
dialogues. Framed as "LLM long-context benchmark #47," it drifts toward COLM.
Framed with the linguistic object first — anaphora resolution under distance,
with typologically varied test languages and a coreference-aware error
taxonomy — it becomes an ACL analysis paper, and the benchmark becomes a
resource contribution with a data statement. Same experiments; the venue fit
is decided by which question the paper asks.
Anti-fit signals worth trusting
- The paper's interest evaporates if a specific commercial model updates —
a snapshot artifact, not a finding about language or method.
- No error analysis is imaginable because outputs are only scores — the
project measured something but cannot yet explain anything.
- The "multilingual" plan is English plus machine-translated test sets with
no native-speaker validation — reviewers treat this as English squared.
- The contribution is a wrapper around an API with prompt engineering as
the method — workshops and system demos exist for exactly this.
- The dataset section cannot answer license and consent questions — fix
the resource before choosing any venue (see
acl-artifact-evaluation).
Questions that settle borderline calls
- Which existing ACL paper would cite this one first, and in which
section — methods, data, or related work? No answer means no audience.
- Does the claim survive being scoped to the tested languages and models?
If the honest scoped version sounds trivial, the work is not done.
- Is the evaluation itself a contribution? If yes, consider leading with
it — evaluation and analysis papers are a strong current at ACL.
- Could the short-paper version carry the whole point? If yes, submitting
long dilutes it across pages reviewers will judge as padding.
Theme-track fine print
- Theme submissions ride the same ARR pipeline and format rules; the theme
is a reviewing lane and award category, not a separate venue.
- Fit is judged on whether the paper answers the theme question, not on
keyword overlap — retrofitting a theme paragraph onto an unrelated paper
is transparent to theme-track reviewers.
- Themes change annually and are announced in each edition's call; never
assume last year's theme (or its reviewer pool) carries over.
Output format
[Fit] strong ACL / possible ACL / sibling venue / non-*ACL venue
[Contribution type] method / resource / evaluation / analysis / theory / position
[Format] long / short / industry / SRW / theme-track
[Claim sentence] <one sentence with the linguistic object named>
[Scoop check] <nearest recent work + standing delta>
[Route decision] <submit cycle X, commit target Y, fallback Z>
Source: brycewang-stanford/Awesome-Journal-Skills → ACL-Skills/skills/acl-topic-selection/SKILL.md
1---2name: acl-topic-selection3description: 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.4---567# ACL Topic Selection89Use this before the first draft. ACL is the flagship of the \*ACL family:10broadest scope across computational linguistics and NLP, the most competitive11main-program bar, and — under ACL Rolling Review — a venue choice you finalize12at *commitment* time, which gives topic strategy an unusual second chance.1314## What ACL rewards1516- A contribution about **language**: modeling it, measuring it, resourcing it,17 or explaining how systems process it — with the linguistic question visible,18 not incidental.19- Typed contributions reviewers can classify fast: method, resource,20 evaluation/metric, analysis, theory, or position. Papers that are half21 method and half unvalidated resource read as neither.22- Evidence proportional to breadth (see `acl-experiments`) and an error23 analysis that says something about language, not just scores.24- Work engaging the current field conversation — for ACL 2026, the special25 theme was explainability of NLP models, with a dedicated Thematic Paper26 Award; each edition names its own theme.2728## Family routing2930| Signal | Better home |31|---|---|32| Core NLP contribution, broad audience, strongest possible reviews wanted | ACL (or whichever \*ACL your ARR package is eligible to commit to) |33| Empirical, engineering-forward NLP; dense experimental papers | EMNLP — historically the empirical sibling, same ARR pipeline |34| Regional relevance, or timing fits its cycle windows | NAACL / EACL / AACL |35| Needs >9 pages, revision-based journal reviewing, no conference clock | TACL (journal, also Anthology-published) |36| Survey-scale or theoretical linguistics depth | Computational Linguistics (journal) |37| LLM-centric work thin on language questions | COLM or an ML venue (NeurIPS/ICML/ICLR) |38| Deployed-system lessons, product constraints | ACL industry track — separate CFP and deadlines |39| Early-stage, student-led | ACL Student Research Workshop |4041Because commitment is decoupled, "ACL vs EMNLP" is often not a submission-time42decision: submit to ARR when ready, then commit to the conference whose window43and bar the finished package fits.4445## Long or short4647- Long (8 pages): a complete arc — method or resource, evaluation, analysis.48- Short (4 pages): one falsifiable point with one decisive experiment; a49 negative result, a focused analysis, an evaluation flaw demonstrated.50 Short papers are judged as short papers — reviewers reject compressed long51 papers but reward genuinely small, sharp claims.5253## Fit sharpening before writing54551. Write the one-sentence claim naming the linguistic object: task,56 phenomenon, language set, or evaluation practice.572. Name the reviewer community: who at ACL wants this answer? If the honest58 answer is "ML engineers," reconsider the venue or reframe toward the59 language question.603. Check the theme track: a solid paper matching the year's theme gains a61 natural reviewer pool and an award lane.624. Stress-test the Findings scenario: would a Findings acceptance satisfy63 the project's goals? If not, ask what would push it into the main program64 — usually analysis depth or evaluation breadth — and plan that now.655. Verify novelty against the last two \*ACL rounds specifically66 (see `acl-related-work`); ACL's most common fit failure is a project67 scooped between conception and cycle deadline.6869## Vignette: routing an LLM evaluation project7071A team measures whether chat models track discourse referents across long72dialogues. Framed as "LLM long-context benchmark #47," it drifts toward COLM.73Framed with the linguistic object first — anaphora resolution under distance,74with typologically varied test languages and a coreference-aware error75taxonomy — it becomes an ACL analysis paper, and the benchmark becomes a76resource contribution with a data statement. Same experiments; the venue fit77is decided by which question the paper asks.7879## Anti-fit signals worth trusting8081- The paper's interest evaporates if a specific commercial model updates —82 a snapshot artifact, not a finding about language or method.83- No error analysis is imaginable because outputs are only scores — the84 project measured something but cannot yet explain anything.85- The "multilingual" plan is English plus machine-translated test sets with86 no native-speaker validation — reviewers treat this as English squared.87- The contribution is a wrapper around an API with prompt engineering as88 the method — workshops and system demos exist for exactly this.89- The dataset section cannot answer license and consent questions — fix90 the resource before choosing any venue (see `acl-artifact-evaluation`).9192## Questions that settle borderline calls93941. Which existing ACL paper would cite this one first, and in which95 section — methods, data, or related work? No answer means no audience.962. Does the claim survive being scoped to the tested languages and models?97 If the honest scoped version sounds trivial, the work is not done.983. Is the evaluation itself a contribution? If yes, consider leading with99 it — evaluation and analysis papers are a strong current at ACL.1004. Could the short-paper version carry the whole point? If yes, submitting101 long dilutes it across pages reviewers will judge as padding.102103## Theme-track fine print104105- Theme submissions ride the same ARR pipeline and format rules; the theme106 is a reviewing lane and award category, not a separate venue.107- Fit is judged on whether the paper *answers* the theme question, not on108 keyword overlap — retrofitting a theme paragraph onto an unrelated paper109 is transparent to theme-track reviewers.110- Themes change annually and are announced in each edition's call; never111 assume last year's theme (or its reviewer pool) carries over.112113## Output format114115```text116[Fit] strong ACL / possible ACL / sibling venue / non-*ACL venue117[Contribution type] method / resource / evaluation / analysis / theory / position118[Format] long / short / industry / SRW / theme-track119[Claim sentence] <one sentence with the linguistic object named>120[Scoop check] <nearest recent work + standing delta>121[Route decision] <submit cycle X, commit target Y, fallback Z>122```123124---125126**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `ACL-Skills/skills/acl-topic-selection/SKILL.md`