论文标题
事件预测的图形增强BERT模型
A Graph Enhanced BERT Model for Event Prediction
论文作者
论文摘要
预测现有事件上下文的后续事件是一项重要但具有挑战性的任务，因为它需要了解事件之间的基本关系。先前的方法建议从事件图检索关系特征，以增强事件相关的建模。但是，事件图的稀疏性可能会限制相关图信息的获取，从而影响模型性能。为了解决此问题，我们考虑使用BERT模型自动构建事件图。为此，我们将附加的结构化变量纳入BERT中，以学习预测培训过程中的事件连接。因此，在测试过程中，可以通过结构化变量预测未见事件的连接关系。结果在两个事件预测任务上：脚本事件预测和故事结束预测，表明我们的方法可以胜过最先进的基线方法。
Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. Previous methods propose to retrieve relational features from event graph to enhance the modeling of event correlation. However, the sparsity of event graph may restrict the acquisition of relevant graph information, and hence influence the model performance. To address this issue, we consider automatically building of event graph using a BERT model. To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process. Hence, in the test process, the connection relationship for unseen events can be predicted by the structured variable. Results on two event prediction tasks: script event prediction and story ending prediction, show that our approach can outperform state-of-the-art baseline methods.
