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A comprehensive guide detailing the strict requirements for submitting research papers to EMNLP via the ACL Rolling Review (ARR). Key areas covered include template compliance, page length restrictions (long/short format), mandatory limitations sections, double-blind anonymization techniques, and adherence to the Responsible NLP checklist. Use this audit guide before submission to ensure full compliance and avoid desk-rejects.

A comprehensive guide for researchers deciding where to submit an empirical NLP paper. It outlines EMNLP's focus on empirical methods, provides an 'empirical-identity test,' and compares submissions across major venues (ACL, ML, CoNLL, etc.) to help authors choose the most suitable program and pitch.

This guide provides a comprehensive strategy for authors submitting NLP and Computational Linguistics papers to EACL. It covers conference positioning, manuscript re-framing from journal or arXiv styles, and establishes rigorous evidence standards. It helps authors ensure their work aligns with the European ACL community focus, optimizing the paper's narrative structure for high acceptance rates.

This guide provides an in-depth framework for assessing the foundational significance of theoretical computer science results. It details the rigorous criteria used by top-tier conferences like FOCS (Foundations of Computer Science) to judge a paper's impact, focusing on its consequence, how it overcomes known obstacles, and its durability beyond best constants. It also maps various research types (e.g., cryptographic, algorithmic, learning theory) to the most suitable venue (e.g., FOCS, STOC, SODA, CCC).

A comprehensive framework for researchers to assess if their AI research contribution is best suited for a major conference like IJCAI, or if a highly specialized venue (such as NeurIPS, ICML, CVPR, or ACL) would provide a clearer audience. It guides decisions based on technical depth, problem scope, and contribution type.

A comprehensive guide for authors to determine the optimal conference venue for their speech-related research. It utilizes a 'modality test' to assess whether a contribution relies on audio signal characteristics (acoustics, prosody, etc.) or merely text transcriptions. The guide helps authors choose between major venues like INTERSPEECH, ACL, ICASSP, and specialized workshops, ensuring the claim is reviewed by relevant experts.

This guide provides comprehensive best practices for packaging and documenting NLP research artifacts (datasets, models, prompts, etc.) for academic submission (e.g., NAACL/ARR). It emphasizes rigorous provenance tracing, ensuring reproducibility, and ethically handling sensitive data, particularly community-owned or indigenous language resources.

This comprehensive guide provides a strategic framework for drafting author responses in academic conference review cycles (e.g., NAACL ARR). It teaches authors how to prioritize reviewer objections based on their downstream impact, structure evidence using coordinates, maintain professional tone, and craft a defensible "frozen record" for the final committee review, maximizing acceptance chances.

This guide details the critical process of transforming an accepted, anonymous conference paper into its final, published, open-access version for the ACL Anthology. It covers the essential 'three-source merge' process: integrating reviewer-promised revisions, restoring author identity and community acknowledgements, correcting metadata, and managing conference logistics. Essential reading for authors submitting to top-tier NLP venues like NAACL.

This guide provides an expert methodology for designing and auditing Natural Language Processing (NLP) experiments for top-tier academic submissions. It instructs researchers on how to map meta-review claims to measurable evidence, ensure budget-fair comparisons, and systematically address potential reviewer probes (scope, fairness, contamination, etc.) to maximize publication rigor.

This comprehensive guide equips authors with a structured framework for writing the Related Work section for top-tier NLP conferences (e.g., NAACL). It emphasizes tracking methodological, data, and task lineages, ensuring citation precision across Findings, workshops, and preprints, and culminating in a single, falsifiable "delta sentence" to precisely position the novel contribution.

This guide outlines the stringent standards for ensuring the reproducibility of Natural Language Processing (NLP) research. It teaches authors how to treat the Responsible NLP checklist as a binding contract by meticulously 'pinning' every variable—including model identifiers, API access dates, prompt strings, random seeds, and evaluation metrics. It details creating a comprehensive reproducibility manifest to make research results verifiable and defensible.