Nlp rebuttal

NLP Rebuttal Scenario Playbook

Translated and adapted from MLNLP-World/Paper-Rebuttal-Tips. 28 recurring reviewer-concern scenarios. Each has four parts: the concern, a bad answer that backfires, a recommended answer template, and the takeaway.

Good rebuttal = Respect + Evidence + Clarity.

How to use

1. Classify each reviewer comment with the router below.
2. Load ONLY the reference file(s) for the matched tips.
3. Adapt the recommended-answer template: replace every placeholder (XXX, A/B/C, Table X) with the paper's real content and freshly computed numbers. Never ship a template verbatim.
4. For overall response structure, ordering, and tone, use the write-rebuttal skill (itemize → brain-dump → draft → revise, the 18 tactics, the neutral-third-party test). These two skills compose: that one shapes the whole response, this one shapes each answer.

Concern router

Reviewer concern sounds like	Tip	Reference
"Too complex", "bag of tricks", "which component matters?"	1	innovation-theory.md
"Not novel", "similar to prior work A"	2	innovation-theory.md
"Just a combination of existing techniques"	3	innovation-theory.md
"Contributions unclear"	4	innovation-theory.md
"Motivation unclear", "why is this problem important?"	5	innovation-theory.md
"No theoretical analysis", "why does it work?"	6	innovation-theory.md
"Limitations discussion is superficial"	7	innovation-theory.md
"Related work missing/insufficient"	8	communication-writing.md
"Writing/notation unclear"	9	communication-writing.md
Reviewer misunderstood the method	10	communication-writing.md
Vague, low-quality negative review	11	communication-writing.md
Tempted to reply "we will add..."	12	communication-writing.md
"Missing/weak baselines"	13	experiments-evidence.md
"Improvements are marginal"	14	experiments-evidence.md
"Unfair experimental setup"	15	experiments-evidence.md
"Missing ablations"	16	experiments-evidence.md
"Too much computational overhead"	17	experiments-evidence.md
Asked for experiments too large for the rebuttal window	18	experiments-evidence.md
"Dataset too small"	19	experiments-evidence.md
"Generalization not shown" (few datasets/models/tasks)	20	experiments-evidence.md
"No variance / significance / seeds"	21	experiments-evidence.md
"Possible train/test leakage or contamination"	22	experiments-evidence.md
"Hyperparameter sensitivity?" ("why k=40?")	23	experiments-evidence.md
"Wrong/missing evaluation metrics"	24	experiments-evidence.md
"No human evaluation"	25	experiments-evidence.md
"Intermediate outputs never evaluated directly"	26	experiments-evidence.md
Claims "continual/online" but experiments are one-shot offline	27	experiments-evidence.md
"No code, seeds, or hyperparameters — not reproducible"	28	experiments-evidence.md

A single comment often maps to several tips (e.g. "marginal gains and no significance testing" = 14 + 21). Load all matches and merge their strategies into one answer.

Cross-cutting rules (from the 28 scenarios)

• Act, don't promise (Tip 12): run the number/analysis now and put it in the rebuttal. "We will add X in the revision" alone convinces nobody.
• Never blame the reviewer (Tips 4, 9, 10): if they misread, the fix is a clarification plus a pointer to the line, stated neutrally.
• Answer head-on (Tips 2, 3): name exactly where the difference or novelty lies — motivation, mechanism, role — not just that it exists.
• Evidence over adjectives (Tips 13-28): every disputed claim gets a table, an ablation, a test, or an honest statement of infeasibility with a scaled-down proxy result (Tip 18).
• Concede real weaknesses gracefully (Tips 7, 14, 19): bound the claim, show the trend, explain what the paper still establishes.

Anti-patterns (never do)

• Asserting all components are necessary without per-component ablation (Tip 1).
• "We are the first to apply X to Y" as the whole novelty defense (Tip 3).
• Repeating the introduction as the answer to a motivation question (Tip 5).
• "Experiments show it works" as the answer to a theory question (Tip 6).
• Calling the setup fair without matching compute/tuning budgets (Tip 15).
• Dismissing a metric request instead of adding the metric (Tip 24).