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 present temporal event reasoning as a supplement 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 is provided to validate its effectiveness in crowdsourcing scenarios.

翻译：时序关系提取旨在把握事件或行为的演变过程，从而塑造关联任务的工作流程，因此在帮助理解众包系统中请求方发起的任务请求方面具有应用前景。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提取的多任务提示学习框架，该框架融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提取的各类要素以实现自动提示生成。此外，我们引入时序事件推理作为补充机制，以增强模型对事件与时间线索的关注。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。案例研究验证了其在众包场景中的有效性。