The representation of events in text plays a significant role in various NLP
tasks. Recent research demonstrates that contrastive learning has the ability
to improve event comprehension capabilities of Pre-trained Language Models
(PLMs) and enhance the performance of event representation learning. However,
the efficacy of event representation learning based on contrastive learning and
PLMs is limited by the short length of event texts. The length of event texts
differs significantly from the text length used in the pre-training of PLMs. As
a result, there is inconsistency in the distribution of text length between
pre-training and event representation learning, which may undermine the
learning process of event representation based on PLMs. In this study, we
present PromptCL, a novel framework for event representation learning that
effectively elicits the capabilities of PLMs to comprehensively capture the
semantics of short event texts. PromptCL utilizes a Prompt template borrowed
from prompt learning to expand the input text during Contrastive Learning. This
helps in enhancing the event representation learning by providing a structured
outline of the event components. Moreover, we propose Subject-Predicate-Object
(SPO) word order and Event-oriented Masked Language Modeling (EventMLM) to
train PLMs to understand the relationships between event components. Our
experimental results demonstrate that PromptCL outperforms state-of-the-art
baselines on event related tasks. Additionally, we conduct a thorough analysis
and demonstrate that using a prompt results in improved generalization
capabilities for event representations. Our code will be available at
this https URL

在本研究中，我们提出了 PromptCL，这是一种用于事件表示学习的新框架，能够有效地发挥预训练语言模型 (PLMs) 综合捕捉短事件文本语义的能力。PromptCL 利用了从提示学习中借鉴的 Prompt 模板，通过在对比学习中扩展输入文本，提供事件组成部分的结构概述，从而提高事件表示学习的效果。此外，我们提出了主谓宾 (SPO) 的词序和面向事件的掩码语言建模 (EventMLM) 来训练 PLMs 理解事件组成部分之间的关系。实验结果表明，PromptCL 在事件相关任务上优于最先进的基准方法，并且我们进行了彻底的分析，证明使用提示可以提高事件表示的泛化能力。