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        <title>Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction</title>
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    <abstract>Script Event Prediction (SEP) aims to forecast the next event in a sequence from a list of candidates. Traditional methods often use pre-trained language models to model event associations but struggle with semantic ambiguity and embedding bias. Semantic ambiguity arises from the multiple meanings of identical words and insufficient consideration of event arguments, while embedding bias results from assigning similar word embeddings to event pairs with similar lexical features, despite their different meanings. To address above issues, we propose a the Semantic and Sentiment Dual-enhanced Generative Model (SSD-GM). SSD-GM leverages two types of script event information to enhance the generative model. Specifically, it employs a GNN-based semantic structure aggregator to integrate the event-centric structure information, thereby mitigating the impact of semantic ambiguity. Furthermore, we find that local sentiment variability effectively reduces biases in event embeddings, while maintaining global sentiment consistency enhances predictive accuracy. As a result, SSD-GM adeptly captures both global and local sentiment of events through its sentiment information awareness mechanism. Extensive experiments on the Multi-Choice Narrative Cloze (MCNC) task demonstrate that our approach achieves better results than other state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction
%A Wu, Feiyang
%A Huang, Peixin
%A Hu, Yanli
%A Tan, Zhen
%A Zhao, Xiang
%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 wu-etal-2025-semantic
%X Script Event Prediction (SEP) aims to forecast the next event in a sequence from a list of candidates. Traditional methods often use pre-trained language models to model event associations but struggle with semantic ambiguity and embedding bias. Semantic ambiguity arises from the multiple meanings of identical words and insufficient consideration of event arguments, while embedding bias results from assigning similar word embeddings to event pairs with similar lexical features, despite their different meanings. To address above issues, we propose a the Semantic and Sentiment Dual-enhanced Generative Model (SSD-GM). SSD-GM leverages two types of script event information to enhance the generative model. Specifically, it employs a GNN-based semantic structure aggregator to integrate the event-centric structure information, thereby mitigating the impact of semantic ambiguity. Furthermore, we find that local sentiment variability effectively reduces biases in event embeddings, while maintaining global sentiment consistency enhances predictive accuracy. As a result, SSD-GM adeptly captures both global and local sentiment of events through its sentiment information awareness mechanism. Extensive experiments on the Multi-Choice Narrative Cloze (MCNC) task demonstrate that our approach achieves better results than other state-of-the-art baselines.
%U https://aclanthology.org/2025.coling-main.622/
%P 9250-9259

Markdown (Informal)

[Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction](https://aclanthology.org/2025.coling-main.622/) (Wu et al., COLING 2025)

• Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (Wu et al., COLING 2025)

ACL

• Feiyang Wu, Peixin Huang, Yanli Hu, Zhen Tan, and Xiang Zhao. 2025. Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction. In Proceedings of the 31st International Conference on Computational Linguistics, pages 9250–9259, Abu Dhabi, UAE. Association for Computational Linguistics.