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    <abstract>Presidential debates are critical platforms for political discourse, yet existing research lacks datasets tailored for analyzing real-time professional commentary. To address this, we introduce the Dataset on Real-time Commentary in U.S. Presidential debates (DR-CUP), which aligns U.S. presidential debate transcripts (2016–2024) with professional commentary and annotations. DR-CUP supports research on commentary understanding, planning, and generation, offering insights into expert analysis and its role in contextualizing complex political discourse. In pilot studies, we evaluated state-of-the-art large language models (LLMs), revealing notable performance differences in understanding expert commentary and planning for generating professional commentary. DR-CUP is the first dataset to incorporate real-time cross-document alignment for debate data, providing a comprehensive resource for advancing research in political communication and computational social science.</abstract>
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%0 Conference Proceedings
%T DR-CUP: A Dataset on Real-time Commentary in U.S. Presidential Debates
%A Chang, Yu-Yu
%A Ho, Huan-Wen
%A Chen, Chung-Chi
%A Wang, Ming-Hung
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F chang-etal-2026-dr
%X Presidential debates are critical platforms for political discourse, yet existing research lacks datasets tailored for analyzing real-time professional commentary. To address this, we introduce the Dataset on Real-time Commentary in U.S. Presidential debates (DR-CUP), which aligns U.S. presidential debate transcripts (2016–2024) with professional commentary and annotations. DR-CUP supports research on commentary understanding, planning, and generation, offering insights into expert analysis and its role in contextualizing complex political discourse. In pilot studies, we evaluated state-of-the-art large language models (LLMs), revealing notable performance differences in understanding expert commentary and planning for generating professional commentary. DR-CUP is the first dataset to incorporate real-time cross-document alignment for debate data, providing a comprehensive resource for advancing research in political communication and computational social science.
%R 10.63317/3xv2rb4s2iyn
%U https://aclanthology.org/2026.lrec-1.772/
%U https://doi.org/10.63317/3xv2rb4s2iyn
%P 9850-9860

Markdown (Informal)

[DR-CUP: A Dataset on Real-time Commentary in U.S. Presidential Debates](https://aclanthology.org/2026.lrec-1.772/) (Chang et al., LREC 2026)

• DR-CUP: A Dataset on Real-time Commentary in U.S. Presidential Debates (Chang et al., LREC 2026)

ACL

• Yu-Yu Chang, Huan-Wen Ho, Chung-Chi Chen, and Ming-Hung Wang. 2026. DR-CUP: A Dataset on Real-time Commentary in U.S. Presidential Debates. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 9850–9860, Palma de Mallorca, Spain. ELRA Language Resource Association.