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.

基于预训练语言模型的多任务提示学习框架（TemPrompt）结合了提示调整和对比学习来解决事件的演变以及任务请求的问题，实验结果表明在标准和少样本情况下，TemPrompt 在多个指标下优于其他基准模型，并通过案例研究验证了在众包场景中的有效性。

TemPrompt: 基于 RAG 的众包系统中的多任务提示学习用于时间关系抽取

TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction  in RAG-based Crowdsourcing Systems

Multi-Task Learning (MTL) is designed to train multiple correlated tasks
simultaneously, thereby enhancing the performance of individual tasks.
Typically, a multi-task network structure consists of a shared backbone and
task-specific decoders. However, the complexity of the decoders increases with
the number of tasks. To tackle this challenge, we integrate the decoder-free
vision-language model CLIP, which exhibits robust zero-shot generalization
capability. Recently, parameter-efficient transfer learning methods have been
extensively explored with CLIP for adapting to downstream tasks, where prompt
tuning showcases strong potential. Nevertheless, these methods solely fine-tune
a single modality (text or visual), disrupting the modality structure of CLIP.
In this paper, we first propose Multi-modal Alignment Prompt (MmAP) for CLIP,
which aligns text and visual modalities during fine-tuning process. Building
upon MmAP, we develop an innovative multi-task prompt learning framework. On
the one hand, to maximize the complementarity of tasks with high similarity, we
utilize a gradient-driven task grouping method that partitions tasks into
several disjoint groups and assign a group-shared MmAP to each group. On the
other hand, to preserve the unique characteristics of each task, we assign an
task-specific MmAP to each task. Comprehensive experiments on two large
multi-task learning datasets demonstrate that our method achieves significant
performance improvements compared to full fine-tuning while only utilizing
approximately 0.09% of trainable parameters.

利用多模态对齐提示（MmAP）和创新的多任务提示学习框架，本文在多任务学习中实现了显著的性能改进，同时仅利用约 0.09％的可训练参数。