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    <abstract>This paper presents our participation in the SemEval-2025 task 6: multinational, multilingual, multi-industry promise verification. The SemEval-2025 Task 6 aims to extract Promise Identification, Supporting Evidence, Clarity of the Promise-Evidence Pair, and Timing for Verification from the commitments made to businesses and governments. Use these data to verify whether companies and governments have fulfilled their commitments. In this task, we participated in the English task, whichincluded analysis of numbers in the text, reading comprehension of the text content and multi-label classification. Our model introduces regularization dropout based on Bert-base to focus on the stability of non-target classes, improve the robustness of the model, and ultimately improve the indicators. Our approach obtained competitive results in subtasks.</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2025 Task 6: Using BERT Model with R-drop for Promise Verification
%A Deng, Dehui
%A Zhang, You
%A Wang, Jin
%A Xu, Dan
%A Zhang, Xuejie
%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 deng-etal-2025-ynu
%X This paper presents our participation in the SemEval-2025 task 6: multinational, multilingual, multi-industry promise verification. The SemEval-2025 Task 6 aims to extract Promise Identification, Supporting Evidence, Clarity of the Promise-Evidence Pair, and Timing for Verification from the commitments made to businesses and governments. Use these data to verify whether companies and governments have fulfilled their commitments. In this task, we participated in the English task, whichincluded analysis of numbers in the text, reading comprehension of the text content and multi-label classification. Our model introduces regularization dropout based on Bert-base to focus on the stability of non-target classes, improve the robustness of the model, and ultimately improve the indicators. Our approach obtained competitive results in subtasks.
%U https://aclanthology.org/2025.semeval-1.248/
%P 1905-1911

Markdown (Informal)

[YNU-HPCC at SemEval-2025 Task 6: Using BERT Model with R-drop for Promise Verification](https://aclanthology.org/2025.semeval-1.248/) (Deng et al., SemEval 2025)

• YNU-HPCC at SemEval-2025 Task 6: Using BERT Model with R-drop for Promise Verification (Deng et al., SemEval 2025)

ACL

• Dehui Deng, You Zhang, Jin Wang, Dan Xu, and Xuejie Zhang. 2025. YNU-HPCC at SemEval-2025 Task 6: Using BERT Model with R-drop for Promise Verification. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1905–1911, Vienna, Austria. Association for Computational Linguistics.