@inproceedings{chen-etal-2025-semeval,
    title = "{S}em{E}val-2025 Task 6: Multinational, Multilingual, Multi-Industry Promise Verification",
    author = "Chen, C h u n g - C h i  and
      Seki, Yohei  and
      Shu, Hakusen  and
      Lhuissier, Anais  and
      Kang, Juyeon  and
      Lee, Hanwool  and
      Day, Min - Yuh  and
      Takamura, Hiroya",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.321/",
    pages = "2461--2471",
    ISBN = "979-8-89176-273-2",
    abstract = "While extensive research exists on misinformation and disinformation, there is limited focus on future-oriented commitments, such as corporate ESG promises, which are often difficult to verify yet significantly impact public trust and market stability. To address this gap, we introduce the task of promise verification, leveraging natural language processing (NLP) techniques to automatically detect ESG commitments, identify supporting evidence, and evaluate the consistency between promises and evidence, while also inferring potential verification time points. This paper presents the dataset used in SemEval-2025 PromiseEval, outlines participant solutions, and discusses key findings. The goal is to enhance transparency in corporate discourse, strengthen investor trust, and support regulators in monitoring the fulfillment of corporate commitments."
}