  Prediction over event sequences is critical for many real-world applications
in Information Retrieval and Natural Language Processing. Future Event
Generation (FEG) is a challenging task in event sequence prediction because it
requires not only fluent text generation but also commonsense reasoning to
maintain the logical coherence of the entire event story. In this paper, we
propose a novel explainable FEG framework, Coep. It highlights and integrates
two types of event knowledge, sequential knowledge of direct event-event
relations and inferential knowledge that reflects the intermediate character
psychology between events, such as intents, causes, reactions, which
intrinsically pushes the story forward. To alleviate the knowledge forgetting
issue, we design two modules, Im and Gm, for each type of knowledge, which are
combined via prompt tuning. First, Im focuses on understanding inferential
knowledge to generate commonsense explanations and provide a soft prompt vector
for Gm. We also design a contrastive discriminator for better generalization
ability. Second, Gm generates future events by modeling direct sequential
knowledge with the guidance of Im. Automatic and human evaluation demonstrate
that our approach can generate more coherent, specific, and logical future
events.
