Integrating Argument-Level Attention with Multi-Level Scores to Predict What Happen Next

Script event prediction is a significant task to extract event evolution rules from text. It requires to capture the intra-rationality and inter-relationship of events. The previous methods have made great progress, but they can not fully capture the inner features of events. This paper proposes an event representation layer with argument-level attention to capture the relationship between predicate and arguments, which benefit to assess the rationality of the event. Besides, we integrate the enhanced event representation with the multi-level event similarity scores to obtain more robust performance. The multi-level similarity consists of scene similarity and semantic consistency. We utilize the similarity between candidates and individual events to evaluate the scene similarity. Semantic consistency is evaluated by the hidden state of Gate Recurrent Unit (GRU). Unlike the previous work, we only use the last hidden state of GRU instead of all hidden states to reduce training time. In addition, we adopt adversarial training to improve the robustness and generalization of the model. The experimental results verify the effectiveness of our model compared to these baseline methods. This paper also demonstrates what the attention mechanism has learned to enhance the interpretability of our model.