Event Causality Identification (ECI) aims at determining whether there is a causal relation between two event mentions. Conventional prompt learning designs a prompt template to first predict an answer word and then maps it to the final decision. Unlike conventional prompts, we argue that predicting an answer word may not be a necessary prerequisite for the ECI task. Instead, we can first make a deterministic assumption on the existence of causal relation between two events and then evaluate its rationality to either accept or reject the assumption. The design motivation is to try the most utilization of the encyclopedia-like knowledge embedded in a pre-trained language model. In light of such considerations, we propose a deterministic assumption prompt learning model, called DAPrompt, for the ECI task. In particular, we design a simple deterministic assumption template concatenating with the input event pair, which includes two masks as predicted events' tokens. We use the probabilities of predicted events to evaluate the assumption rationality for the final event causality decision. Experiments on the EventStoryLine corpus and Causal-TimeBank corpus validate our design objective in terms of significant performance improvements over the state-of-the-art algorithms.

翻译：摘要：事件因果关系识别旨在判定两个事件提及之间是否存在因果关系。传统提示学习通过设计提示模板先预测答案词，再将其映射至最终决策。不同于传统提示方法，我们认为对于因果关系识别任务而言，预测答案词并非必要前提。相反，可先对两事件间是否存在因果关系做出确定性假设，再评估其合理性以决定接受或拒绝该假设。这一设计思路源于最大化利用预训练语言模型中蕴含的类百科全书知识。基于此考量，我们提出用于因果关系识别任务的确定性假设提示学习模型DAPrompt。具体而言，我们设计了与输入事件对拼接的简洁确定性假设模板，该模板包含两个掩码作为预测事件的标记token。通过预测事件概率评估假设合理性，最终做出事件因果关系判定。在EventStoryLine语料库和Causal-TimeBank语料库上的实验表明，相较于现有最优算法，本方法的性能提升显著验证了设计目标的可行性。