Envisioning a Future Beyond Tomorrow with Script Event Stream Prediction

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.