Crowdsourcing is a critical technology in social manufacturing, which
leverages an extensive and boundless reservoir of human resources to handle a
wide array of complex tasks. The successful execution of these complex tasks
relies on task decomposition (TD) and allocation, with the former being a
prerequisite for the latter. Recently, pre-trained language models (PLMs)-based
methods have garnered significant attention. However, they are constrained to
handling straightforward common-sense tasks due to their inherent restrictions
involving limited and difficult-to-update knowledge as well as the presence of
hallucinations. To address these issues, we propose a retrieval-augmented
generation-based crowdsourcing framework that reimagines TD as event detection
from the perspective of natural language understanding. However, the existing
detection methods fail to distinguish differences between event types and
always depend on heuristic rules and external semantic analyzing tools.
Therefore, we present a Prompt-Based Contrastive learning framework for TD
(PBCT), which incorporates a prompt-based trigger detector to overcome
dependence. Additionally, trigger-attentive sentinel and masked contrastive
learning are introduced to provide varying attention to trigger and contextual
features according to different event types. Experiment results demonstrate the
competitiveness of our method in both supervised and zero-shot detection. A
case study on printed circuit board manufacturing is showcased to validate its
adaptability to unknown professional domains.

通过引入基于提示的对比学习，我们提出了一种用于任务分解的检测框架，能够有效地区分不同事件类型，并在多个领域中具有竞争力的性能。