TemPrompt：面向基于RAG的众包系统中时间关系抽取的多任务提示学习
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
- National University of Defense Technology（中国人民解放军国防科技大学）
- Zhejiang Lab（之江实验室）
- School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）
- University of Macau（澳门大学）
- Faculty of Innovation Engineering, Macau University of Science and Technology（澳门科技大学创新工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对众包系统中时间关系抽取面临的标注数据有限且分布不均问题，提出融合提示调优与对比学习的多任务提示框架TemPrompt，在标准和少样本设置下性能优于基线，且在众包场景有效。
AI中文摘要：
时间关系抽取（TRE）旨在把握事件或动作的演化，进而梳理相关任务的工作流，因此有望助力理解众包系统中请求者发起的任务需求。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（PLMs）存储的丰富全局知识启发，我们提出一种面向TRE的多任务提示学习框架TemPrompt，融合提示调优与对比学习以解决上述问题。为给PLMs引导出更有效的提示，我们提出一种面向任务的提示构建方法，该方法充分考虑TRE的各类因素以自动生成提示。此外，我们将掩码语言建模形式的时间事件推理设计为辅助任务，以增强模型对事件和时间线索的关注度。实验结果表明，在标准设置和少样本设置下，TemPrompt在多数指标上均优于所有对比基线。我们还通过一个印刷电路板设计与制造的案例研究，验证了其在众包场景中的有效性。
英文摘要：
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.
