@inproceedings{chen-etal-2025-semeval,
title = "{S}em{E}val-2025 Task 6: Multinational, Multilingual, Multi-Industry Promise Verification",
author = {Chen, Chung-Chi and
Seki, Yohei and
Shu, Hakusen and
Lhuissier, Ana{\"i}s 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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T SemEval-2025 Task 6: Multinational, Multilingual, Multi-Industry Promise Verification
%A Chen, Chung-Chi
%A Seki, Yohei
%A Shu, Hakusen
%A Lhuissier, Anaïs
%A Kang, Juyeon
%A Lee, Hanwool
%A Day, Min-Yuh
%A Takamura, Hiroya
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F chen-etal-2025-semeval
%X 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.
%U https://aclanthology.org/2025.semeval-1.321/
%P 2461-2471
Markdown (Informal)
[SemEval-2025 Task 6: Multinational, Multilingual, Multi-Industry Promise Verification](https://aclanthology.org/2025.semeval-1.321/) (Chen et al., SemEval 2025)
ACL
- Chung-Chi Chen, Yohei Seki, Hakusen Shu, Anaïs Lhuissier, Juyeon Kang, Hanwool Lee, Min-Yuh Day, and Hiroya Takamura. 2025. SemEval-2025 Task 6: Multinational, Multilingual, Multi-Industry Promise Verification. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2461–2471, Vienna, Austria. Association for Computational Linguistics.