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.

该论文提出了一种新型的可解释的未来事件生成框架 Coep，该框架集成了两种类型的事件知识，包括直接事件 - 事件关系的序列知识和反映事件之间的心理联系的推理知识，并设计了两个模块 Im 和 Gm，以帮助生成未来事件。

什么推动了故事发展？从常识推断出作为未来事件生成提示的解释

What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation

We present ATOMIC, an atlas of everyday commonsense reasoning, organized
through 877k textual descriptions of inferential knowledge. Compared to
existing resources that center around taxonomic knowledge, ATOMIC focuses on
inferential knowledge organized as typed if-then relations with variables
(e.g., "if X pays Y a compliment, then Y will likely return the compliment").
We propose nine if-then relation types to distinguish causes vs. effects,
agents vs. themes, voluntary vs. involuntary events, and actions vs. mental
states. By generatively training on the rich inferential knowledge described in
ATOMIC, we show that neural models can acquire simple commonsense capabilities
and reason about previously unseen events. Experimental results demonstrate
that multitask models that incorporate the hierarchical structure of if-then
relation types lead to more accurate inference compared to models trained in
isolation, as measured by both automatic and human evaluation.

ATOMIC 是一种以常识推理为组织形式的图表，其中包括了 877k 个基于变量的 if-then 关系类型，使用这种类型，神经模型可以获得简单的常识能力并进行推理，证实了将多任务模型引入到 if-then 关系结构中可以得到比独立训练的模型更准确的推论。