Skill UI

A comprehensive guide for NLP researchers on strategically positioning their work before writing. It teaches how to determine the core linguistic contribution, select the optimal academic venue (ACL, EMNLP, etc.), manage the scope (long vs. short papers), and align the research focus with specific conference themes and review expectations, maximizing publication impact.

A comprehensive guide for revising NLP and Computational Linguistics manuscripts for submission to top-tier conferences like ACL. It mandates adopting a task-first, evidence-grounded approach, strictly scoping claims in the LLM era, and ensuring linguistic rigor in all examples and analyses. Learn how to structure, compress, and refine your arguments for maximum impact.

A comprehensive guide for researchers to determine if their work represents a genuine cross-modal or media-systems contribution suitable for ACM Multimedia (ACM MM). It helps route projects by testing for cross-modality, media system advances, or human-centric results, distinguishing between pure CV, NLP, or system-heavy claims.

A comprehensive guide for authors submitting papers to the ACL conference. It assists in positioning the manuscript, verifying topic fit (NLP, language models, semantics), and ensuring the evidence meets the high standards required for top-tier computational linguistics venues. Covers structure, best practices, and submission checklists.

A comprehensive guide detailing how to structure and populate the supplementary material section of technical research papers, especially in NLP and Machine Learning. It distinguishes between load-bearing elements (main text) and audit trails (appendix), covering prompt verbatim, decoding configurations, full result grids, and code anonymization protocols to ensure high reproducibility and academic rigor.

This skill helps authors strategically decide the optimal venue for their Language Model research. It provides the critical "object-of-study test" to determine if the LM itself is the subject of the study, or merely a tool for a task. Furthermore, it guides routing against top-tier NLP/AI conferences (ACL, NeurIPS, ICLR, etc.), helping authors target the right audience and maximizing submission chances.

A comprehensive strategic guide for authors targeting the Conference and Labs of the Evaluation Forum (CLEF). It helps frame IR/NLP research by defining the appropriate contribution type, evidence bar, and rigorous evaluation methods required for top-tier academic submission, ensuring the paper aligns with the venue's specific community expectations.

A comprehensive guide for positioning Natural Language Processing (NLP) research papers for the Conference on Computational Natural Language Learning (CoNLL). It helps authors optimize their manuscripts by focusing on conference fit, structuring evidence, identifying necessary baselines, and adapting the narrative for a selective CS conference audience. Essential for submission strategy and manuscript re-framing.

This skill guides authors on how to position, structure, and strengthen empirical Natural Language Processing (NLP) manuscripts for submission to the Conference on Empirical Methods in Natural Language Processing (EMNLP). It provides deep insights into conference fit, required evidence (baselines, error analysis, datasets), proper framing, and anticipating reviewer expectations for top-tier AI venues.

A comprehensive guide for preparing research artifacts (code, data, prompts, models) for EACL submissions. It details the two stages: an anonymized supplement for the initial review (ARR) and a fully licensed, public release for post-acceptance. Emphasis is placed on reproducibility, responsible NLP practices, and proper documentation.

A comprehensive guide for authors addressing the rebuttal window for major NLP/AI conferences (like EACL). It details how to strategically triage reviewer objections, craft responses for both individual reviewers and the action editor's meta-review, plan runnable in-window experiments, and make critical decisions about the paper's future venue.

This comprehensive guide outlines the critical steps required to prepare a camera-ready version of an NLP conference paper (e.g., EACL). It covers mandatory submissions after acceptance, including content expansion, de-anonymization (restoring author names, citations, and metadata), addressing meta-review required changes, ensuring correct ACL Anthology metadata, and managing public artifact release and presentation logistics. Essential for ensuring academic integrity and compliance.