C h u n g - C h i Chen
2025
SemEval-2025 Task 6: Multinational, Multilingual, Multi-Industry Promise Verification
C h u n g - C h i Chen
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Yohei Seki
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Hakusen Shu
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Anais Lhuissier
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Juyeon Kang
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Hanwool Lee
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Min - Yuh Day
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Hiroya Takamura
Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
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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- Min - Yuh Day 1
- Juyeon Kang 1
- Hanwool Lee 1
- Anaïs Lhuissier 1
- Yohei Seki 1
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