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.

翻译：时序关系抽取旨在把握事件或行为的演化过程，从而塑造关联任务的工作流，因此在帮助理解众包系统中请求方发起的任务需求方面具有潜力。然而，现有方法仍受限于标注数据有限且分布不均的问题。为此，受预训练语言模型中存储的丰富全局知识启发，我们提出了一种用于时序关系抽取的多任务提示学习框架，该框架结合提示调优与对比学习以应对上述挑战。为从预训练语言模型中激发更有效的提示，我们提出了一种面向任务的提示构建方法，该方法全面考量时序关系抽取的诸多因素以实现自动提示生成。此外，我们设计了以掩码语言建模形式呈现的时序事件推理作为辅助任务，以增强模型对事件及时序线索的关注。实验结果表明，在标准设置与少样本设置下，TemPrompt在多数评估指标上均优于所有基线模型。本文还提供了印刷电路板设计与制造的案例研究，以验证其在众包场景中的有效性。