What Comes Next and Why? A Staged Encoder-Decoder Architecture for Script Event Prediction

A script, which describes the evolutionary path of events, is a structured event sequence. Script event prediction aims to predict the next event from a sequence of historical events. Current studies favor modeling macroscale information, including event sequence, event unit, and event argument, whi...

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Bibliographic Details
Published in:Proceedings of ... International Joint Conference on Neural Networks pp. 1 - 9
Main Authors: Han, Shuaihu, Yang, Guohua, Zhang, Dawei, Tao, Jianhua
Format: Conference Proceeding
Language:English
Published: IEEE 30.06.2024
Subjects:
Accuracy
Aggregates
Data mining
Fuses
information fusion
microscale information
Neural networks
quantitative analysis
script event prediction
Statistical analysis
Vocabulary
ISSN:2161-4407
Online Access:Get full text
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Summary:A script, which describes the evolutionary path of events, is a structured event sequence. Script event prediction aims to predict the next event from a sequence of historical events. Current studies favor modeling macroscale information, including event sequence, event unit, and event argument, while ignoring the smallest unit of event, i.e., argument vocabulary, which we refer to as microscale information. To fuse event information from different scales, we propose a staged encoder-decoder architecture (SEDA) for script event prediction. SEDA aggregates microscale information to enhance the representation of event arguments and event units in the event-enhancement stage, and extracts the context of event sequence to predict the most relevant candidate event in the context-extraction stage. Both stages of SEDA adopt an efficient and scalable encoder-decoder architecture. The experimental results demonstrate that the accuracy of SEDA on the MCNC task surpasses that of the current SOTA baseline. Additionally, we empoly a method based on the Shapley value to calculate the importance of different event units in an event sequence, providing a quantitative analysis of the prediction results.
ISSN:2161-4407
DOI:10.1109/IJCNN60899.2024.10651421