  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
https://github.com/YuboFeng2023/PromptCL.
