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Open Access | Just Accepted

Envisioning a Future Beyond Tomorrow with Script Event Stream Prediction

Zhiyi Fang1Zhuofeng Li2Qingyong Zhang1Changhua Xu3Pinzhuo Tian1( )Shaorong Xie1

1 School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

2 School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

3 School of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia

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Abstract

Script event stream prediction is a task that predicts events based on a given context or script. Most existing methods predict one subsequent event, limiting the ability to make a longer inference about the future. Moreover, external knowledge has been proven to be beneficial for event prediction and used in many methods in the form of relations between events. However, these methods focus mainly on the continuity of actions while ignoring the other components of events. To tackle these issues, we propose a Multi-step Script Event Prediction (MuSEP) method that can make a longer inference according to the given events. We adopt reinforcement learning to implement the multi-step prediction by treating the process as a Markov chain and setting the reward considering both chain-level and event-level thus ensuring the overall quality of prediction results. Additionally, we learn the representations of events with external knowledge which could better understand events and their components. Experimental results on four datasets demonstrate that our method not only outperforms state-of-the-art methods on one-step prediction but is also capable of making multi-step prediction.

Tsinghua Science and Technology
Cite this article:
Fang Z, Li Z, Zhang Q, et al. Envisioning a Future Beyond Tomorrow with Script Event Stream Prediction. Tsinghua Science and Technology, 2025, https://doi.org/10.26599/TST.2024.9010158

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Received: 27 April 2024
Revised: 08 July 2024
Accepted: 25 August 2024
Available online: 08 January 2025

© The author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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