Multi-task learning for various real-world applications usually involves tasks with logical sequential dependence. For example, in online marketing, the cascade behavior pattern of $impression \rightarrow click \rightarrow conversion$ is usually modeled as multiple tasks in a multi-task manner, where the sequential dependence between tasks is simply connected with an explicitly defined function or implicitly transferred information in current works. These methods alleviate the data sparsity problem for long-path sequential tasks as the positive feedback becomes sparser along with the task sequence. However, the error accumulation and negative transfer will be a severe problem for downstream tasks. Especially, at the beginning stage of training, the optimization for parameters of former tasks is not converged yet, and thus the information transferred to downstream tasks is negative. In this paper, we propose a prior information merged model (\textbf{PIMM}), which explicitly models the logical dependence among tasks with a novel prior information merged (\textbf{PIM}) module for multiple sequential dependence task learning in a curriculum manner. Specifically, the PIM randomly selects the true label information or the prior task prediction with a soft sampling strategy to transfer to the downstream task during the training. Following an easy-to-difficult curriculum paradigm, we dynamically adjust the sampling probability to ensure that the downstream task will get the effective information along with the training. The offline experimental results on both public and product datasets verify that PIMM outperforms state-of-the-art baselines. Moreover, we deploy the PIMM in a large-scale FinTech platform, and the online experiments also demonstrate the effectiveness of PIMM.

翻译：面向真实世界应用的多任务学习通常涉及具有逻辑顺序依赖关系的任务。例如，在线营销中，从$impression \rightarrow click \rightarrow conversion$的级联行为模式通常以多任务方式建模为多个子任务，现有工作中任务间的顺序依赖关系仅通过显式定义的函数或隐式传递的信息进行简单连接。这些方法缓解了长路径顺序任务的数据稀疏问题（因为正反馈随任务序列逐渐稀疏），但下游任务会出现严重的错误累积和负迁移问题。尤其是在训练初期，前序任务的参数优化尚未收敛，导致传递给下游任务的信息存在负效应。本文提出先验信息融合模型（\textbf{PIMM}），通过创新的先验信息融合（\textbf{PIM}）模块，以课程学习方式显式建模多个顺序依赖任务间的逻辑依赖关系。具体而言，PIM在训练过程中采用软采样策略，随机选择真实标签信息或前序任务预测结果传递给下游任务。遵循由易到难的课程学习范式，我们动态调整采样概率，确保下游任务在训练过程中能持续获取有效信息。在公开数据集和产品数据集上的离线实验结果表明，PIMM优于当前最先进的基线模型。此外，我们在大规模金融科技平台部署了PIMM，在线实验也验证了其有效性。