Script Event Prediction Based on Causal Generalization Learning
Tianfu Pang, Yingchi Mao, Silong Ding, Biao Wang, Rongzhi Qi · 2023
Causal relationships between events can reflect the historical evolution of events and provide an important reference for predicting future trends. Script prediction methods based on event graphs often struggle to adequately consider the complex interdependencies among events, leading to prediction biases. The Script Event Prediction Based on Causal Generalization Learning (SEPCG) method has been proposed to enhance the accuracy of script event prediction. SEPCG uses the graph attention network to learn the direct causal relationship similarity between known events and candidate events, the direct result event similarity between known events and candidate events, and utilizes double similarity generalization to learn the predicate type between known events and candidate events. SEPCG uses a Neural Tensor Network to learn parameter-level event embeddings and improve the model’s sensitivity to parameter-level changes. Finally, based on the generalized event embeddings, the BiLSTM network is used to simultaneously learn the forward contextual information from the known event to the candidate event direction, i.e., the cause to the result information, and the reverse contextual information from the candidate event to the known event direction, i.e., the result to the cause information. The BiLSTM is used to capture the temporal information of event chains at different levels. The effectiveness of the model is verified on the NYT dataset, with a 1.84% improvement in accuracy compared to the best baseline.