Dear Corpora list members, I am sharing this CFP on the behalf of the Program Chairs of INLG 2025. Apologies in advance for any cross-posting.
Saad. ------- I am excited to share that we will have some great Keynote Speakers this year, including Verena Rieser (Google DeepMind), Minlie Huang (黄民烈; Tsinghua University), Hadas Kotek (Apple), Mike White (Ohio State University) and Iryna Gurevych (TU Darmstadt). The event will be held from October 29 - November 2 in Hanoi, Vietnam, just before EMNLP 2025 in Suzhou, China. Please note, we have decided to extend the paper submission deadline to July 18, 2025. Third Call for Papers: 18th International Natural Language Generation Conference INLG 2025 We invite the submission of long and short papers, as well as system demonstrations, related to all aspects of Natural Language Generation (NLG), including Large Language Models (LLMs). Accepted papers will be presented as oral talks or posters. The event is organized under the auspices of the Special Interest Group on Natural Language Generation (SIGGEN) (https://aclweb.org/aclwiki/SIGGEN) of the Association for Computational Linguistics (ACL) (https://aclweb.org/). The event will be held from October 29 - November 2 in Hanoi, Vietnam. INLG 2025 will be taking place before EMNLP 2025 (5-9 November) in Suzhou, China. Important dates - All deadlines are Anywhere on Earth (UTC-12) START system regular paper submission deadline: July 18, 2025 (NEW) ARR commitment to INLG deadline via START system: August 7, 2025 START system demo paper submission deadline: July 24, 2025 Notification: August 21, 2025 Camera ready: September 15, 2025 Conference: October 29 - November 2, 2025 Topics INLG 2025 solicits papers on any topic related to NLG. General topics of interest include, but are not limited to: Large Language Models (LLMs) for NLG Evaluation and error analysis of NLG systems Explainability and Trustworthiness of NLG systems Generalizability of NLG systems Bias and fairness in NLG systems Thinking models for NLG Affect/emotion generation Analysis and detection of automatically generated text Cognitive modeling of language production Computational efficiency of NLG models Content and text planning Corpora and resources for NLG Ethical considerations of NLG Grounded language generation Lexicalisation Multimedia and multimodality in generation Natural language understanding techniques for NLG NLG and accessibility NLG in speech synthesis and spoken language models NLG in dialogue systems and chatbots NLG for human-robot interaction NLG for low-resourced languages NLG for real-world applications Paraphrasing, summarization and translation Personalisation and variation in text Referring expression generation Storytelling and narrative generation Surface realization System architectures Submissions & Format Three kinds of papers can be submitted: Long papers are most appropriate for presenting substantial research results and must not exceed eight (8) pages of content, plus unlimited pages of ethical considerations, supplementary material statements, and references. The supplementary material statement provides detailed descriptions to support the reproduction of the results presented in the paper (see below for details). The final versions of long papers will be given one additional page of content (up to 9 pages) so that reviewers' comments can be taken into account. Short papers are more appropriate for presenting an ongoing research effort and must not exceed four (4) pages, plus unlimited pages of ethical considerations, supplementary material statements, and references. The final versions of short papers will be given one additional page of content (up to 5 pages) so that reviewers' comments can be taken into account. Demo papers should be no more than two (2) pages, including references, and should describe implemented systems relevant to the NLG community. It also should include a link to a short screencast of the working software. In addition, authors of demo papers must be willing to present a demo of their system during INLG 2025. Submissions should follow ACL Author Guidelines (https://www.aclweb.org/adminwiki/index.php?title=ACL_Author_Guidelines) and policies for submission, review and citation, and be anonymised for double blind reviewing. Please use ACL 2023 style files; LaTeX style files and Microsoft Word templates are available at https://acl-org.github.io/ACLPUB/formatting.html Authors must honor the ethical code set out in the ACL Code of Ethics (https://www.aclweb.org/portal/content/acl-code-ethics). If your work raises any ethical issues, you should include an explicit discussion of those issues. This will also be taken into account in the review process. You may find the following checklist of use: https://aclrollingreview.org/responsibleNLPresearch/ Authors are strongly encouraged to ensure that their work is reproducible; see, e.g., the following reproducibility checklist (https://2021.aclweb.org/calls/reproducibility-checklist/). Papers involving any kind of experimental results (human judgments, system outputs, etc) should incorporate a data availability statement into their paper. Authors are asked to indicate whether the data is made publicly available. If the data is not made available, authors should provide a brief explanation why. (E.g. because the data contains proprietary information.) A statement guide is available on the INLG 2025 website: https://inlg2025.github.io/ To submit a long or short paper to INLG 2025, authors can either submit directly or commit a paper previously reviewed by ARR via the same paper submission site (https://softconf.com/p/inlg2025/). For direct submissions, the deadline for submitting papers is July 18, 2025 (NEW), 11:59:59 AOE. If committing an ARR paper to INLG, the submission is also made through the INLG 2025 paper submission site, indicating the link of the paper on OpenReview. The deadline for committing an ARR paper to INLG is August 7, 2025, 11:59:59 AOE, and the last eligible ARR paper submission deadline for INLG 2025 is May 19, 2025. It is important to note that when committing an ARR paper to INLG, it should be submitted through the INLG 2025 paper submission site, just like a direct submission paper, with the only difference being the need to provide the OpenReview link to the paper and to provide an optional author response to reviews. Demo papers should be submitted directly through the INLG 2025 paper submission site (https://softconf.com/n/inlg2025/) by July 24, 2025, 11:59:59 AOE. All accepted papers will be published in the INLG 2025 proceedings and included in the ACL anthology. A paper accepted for presentation at INLG 2025 must not have been presented at any other meeting with publicly available proceedings. Dual submission to other conferences is permitted, provided that authors clearly indicate this in the submission form. If the paper is accepted at both venues, the authors will need to choose which venue to present at, since they can not present the same paper twice. Finally, at least one of the authors of an accepted paper must register to attend the conference. Awards INLG 2025 will present several awards to recognize outstanding achievements in the field. These awards are: Best Long Paper Award: This award will be given to the best long paper submission based on its originality, impact, and contribution to the field of NLG. Best Short Paper Award: This award will be given to the best short paper submission based on its originality, impact, and contribution to the field of NLG. Best Demo Paper Award: This award will recognize the best demo paper submitted to the conference. This award considers not only the paper's quality but also the demonstration given at the conference. The demonstration will play a significant role in the judging process. Best Evaluation Award: This award honors the authors who have demonstrated the most comprehensive and insightful analysis in evaluating their results. This award aims to highlight papers where the authors have gone the extra mile in providing a thorough and detailed analysis of their results, offering a nuanced understanding of their findings.
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