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    <abstract>The advancement of text generation models has granted us the capability to produce coherent and convincing text on demand. Yet, in real-life circumstances, individuals do not continuously generate text or voice their opinions. For instance, consumers pen product reviews after weighing the merits and demerits of a product, and professional analysts issue reports following significant news releases. In essence, opinion expression is typically prompted by particular reasons or signals. Despite long-standing developments in opinion mining, the appropriate timing for expressing an opinion remains largely unexplored. To address this deficit, our study introduces an innovative task - the identification of news-triggered opinion expressing timing. We ground this task in the actions of professional stock analysts and develop a novel dataset for investigation. Our Generator-Assisted Decision-Focused Approach (GADFA) is decision-focused, leveraging text generation models to steer the classification model, thus enhancing overall performance. Our experimental findings demonstrate that the text generated by our model contributes fresh insights from various angles, effectively aiding in identifying the optimal timing for opinion expression.</abstract>
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%0 Conference Proceedings
%T GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification
%A Chen, Chung-Chi
%A Takamura, Hiroya
%A Kobayashi, Ichiro
%A Miyao, Yusuke
%A Chen, Hsin-Hsi
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F chen-etal-2025-gadfa
%X The advancement of text generation models has granted us the capability to produce coherent and convincing text on demand. Yet, in real-life circumstances, individuals do not continuously generate text or voice their opinions. For instance, consumers pen product reviews after weighing the merits and demerits of a product, and professional analysts issue reports following significant news releases. In essence, opinion expression is typically prompted by particular reasons or signals. Despite long-standing developments in opinion mining, the appropriate timing for expressing an opinion remains largely unexplored. To address this deficit, our study introduces an innovative task - the identification of news-triggered opinion expressing timing. We ground this task in the actions of professional stock analysts and develop a novel dataset for investigation. Our Generator-Assisted Decision-Focused Approach (GADFA) is decision-focused, leveraging text generation models to steer the classification model, thus enhancing overall performance. Our experimental findings demonstrate that the text generated by our model contributes fresh insights from various angles, effectively aiding in identifying the optimal timing for opinion expression.
%U https://aclanthology.org/2025.coling-main.718/
%P 10781-10794

Markdown (Informal)

[GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification](https://aclanthology.org/2025.coling-main.718/) (Chen et al., COLING 2025)

• GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification (Chen et al., COLING 2025)

ACL

• Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi, Yusuke Miyao, and Hsin-Hsi Chen. 2025. GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10781–10794, Abu Dhabi, UAE. Association for Computational Linguistics.