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 impressionβclickβconversion 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