  Temporal relation extraction (TRE) aims to grasp the evolution of events or
actions, and thus shape the workflow of associated tasks, so it holds promise
in helping understand task requests initiated by requesters in crowdsourcing
systems. However, existing methods still struggle with limited and unevenly
distributed annotated data. Therefore, inspired by the abundant global
knowledge stored within pre-trained language models (PLMs), we propose a
multi-task prompt learning framework for TRE (TemPrompt), incorporating prompt
tuning and contrastive learning to tackle these issues. To elicit more
effective prompts for PLMs, we introduce a task-oriented prompt construction
approach that thoroughly takes the myriad factors of TRE into consideration for
automatic prompt generation. In addition, we design temporal event reasoning in
the form of masked language modeling as auxiliary tasks to bolster the model's
focus on events and temporal cues. The experimental results demonstrate that
TemPrompt outperforms all compared baselines across the majority of metrics
under both standard and few-shot settings. A case study on designing and
manufacturing printed circuit boards is provided to validate its effectiveness
in crowdsourcing scenarios.
