  Exploiting label hierarchies has become a promising approach to tackling the
zero-shot multi-label text classification (ZS-MTC) problem. Conventional
methods aim to learn a matching model between text and labels, using a graph
encoder to incorporate label hierarchies to obtain effective label
representations \cite{rios2018few}. More recently, pretrained models like BERT
\cite{devlin2018bert} have been used to convert classification tasks into a
textual entailment task \cite{yin-etal-2019-benchmarking}. This approach is
naturally suitable for the ZS-MTC task. However, pretrained models are
underexplored in the existing work because they do not generate individual
vector representations for text or labels, making it unintuitive to combine
them with conventional graph encoding methods. In this paper, we explore to
improve pretrained models with label hierarchies on the ZS-MTC task. We propose
a Reinforced Label Hierarchy Reasoning (RLHR) approach to encourage
interdependence among labels in the hierarchies during training. Meanwhile, to
overcome the weakness of flat predictions, we design a rollback algorithm that
can remove logical errors from predictions during inference. Experimental
results on three real-life datasets show that our approach achieves better
performance and outperforms previous non-pretrained methods on the ZS-MTC task.
