  Few-Shot Text Classification (FSTC) imitates humans to learn a new text
classifier efficiently with only few examples, by leveraging prior knowledge
from historical tasks. However, most prior works assume that all the tasks are
sampled from a single data source, which cannot adapt to real-world scenarios
where tasks are heterogeneous and lie in different distributions. As such,
existing methods may suffer from their globally knowledge-shared mechanisms to
handle the task heterogeneity. On the other hand, inherent task relation are
not explicitly captured, making task knowledge unorganized and hard to transfer
to new tasks. Thus, we explore a new FSTC setting where tasks can come from a
diverse range of data sources. To address the task heterogeneity, we propose a
self-supervised hierarchical task clustering (SS-HTC) method. SS-HTC not only
customizes cluster-specific knowledge by dynamically organizing heterogeneous
tasks into different clusters in hierarchical levels but also disentangles
underlying relations between tasks to improve the interpretability. Extensive
experiments on five public FSTC benchmark datasets demonstrate the
effectiveness of SS-HTC.
