Skill UI

This guide outlines best practices for structuring advanced NLP/AI research papers for academic submission. It details the critical differences between core content pages, mandatory limitations sections, discretionary appendices, and supplementary uploads, ensuring that reviewers are properly guided and that the work remains reproducible and verifiable.

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.

A comprehensive guide detailing the empirical writing style required for EMNLP submissions. It emphasizes scoping claims to specific datasets and languages, ensuring examples are load-bearing, structuring the argument phenomenon-first, and accurately calibrating hedging language based on evidence strength.

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 skill guides authors on strategically positioning their AI/ML manuscripts for the European Conference on Artificial Intelligence (ECAI). It provides deep insights into the conference's scope, required contribution framing (e.g., algorithm, system, theorem), evidence standards, and how to tailor the narrative for a general European AI readership. Use it when targeting ECAI or needing to refine a paper from a journal or arXiv style into a selective conference submission.

A comprehensive guide to preparing accountability artifacts for AI research papers, particularly for venues like FAccT. It covers the necessary documentation genres—including datasheets for datasets, model cards for models, and impact assessments—to ensure transparency, verifiability, and accountability regarding claims of fairness and harm mitigation in AI systems. The focus is on making research outputs rigorously traceable and ethically responsible.

This comprehensive guide outlines the critical steps required for authors preparing an accepted paper for ACM FAccT proceedings. It details the transition from anonymous drafts to the final, published 'camera-ready' format, covering mandatory de-anonymization (restoring author details and adding Positionality statements), switching to the specific ACM metadata standards (ORCID, CCS concepts), completing required documentation (datasheets/model cards), and adhering to complex publication timelines and rights requirements.

A comprehensive audit guide for submitting academic papers to the FAccT conference. It details critical, multi-step processes, including the mandatory abstract registration gate, maintaining mutual anonymity, adhering to strict ACM formatting rules, and including required endmatter statements (such as the Generative AI Usage Statement).

A comprehensive project management guide for submitting interdisciplinary, responsible AI papers to major academic conferences like FAccT. It provides a detailed backward-planning timeline, covering milestones from initial abstract registration and literature review to revisions, camera-ready submission, and conference presentation, emphasizing ethical considerations and structured academic processes.

This comprehensive guide helps authors navigate the complex decision of where to submit their Human-Robot Interaction (HRI) research. It outlines critical criteria, including the robot-embodiment and human-in-the-loop tests, to determine the correct venue (e.g., HRI, CHI, ICRA/IROS). Furthermore, it details the five specialized tracks within HRI and advises authors on matching their primary technical contribution to the most suitable academic domain.

A comprehensive guide detailing the critical workflow from paper acceptance to final camera-ready submission for major computational conferences (like ICCV). It covers technical requirements such as de-anonymization, synchronizing multiple public copies (arXiv, CVF, IEEE), updating URLs, and managing logistical details for presentation.

A comprehensive guide for structuring the "Related Work" section for top-tier computer vision conferences (ICCV, CVPR, ECCV). It covers best practices for citing intervening-cycle literature, managing concurrent preprints, ensuring double-blind anonymity, and correctly attributing source venues to maximize novelty perception and score highly with reviewers.