  Training deep learning models with limited labelled data is an attractive
scenario for many NLP tasks, including document classification. While with the
recent emergence of BERT, deep learning language models can achieve reasonably
good performance in document classification with few labelled instances, there
is a lack of evidence in the utility of applying BERT-like models on long
document classification. This work introduces a long-text-specific model -- the
Hierarchical BERT Model (HBM) -- that learns sentence-level features of the
text and works well in scenarios with limited labelled data. Various evaluation
experiments have demonstrated that HBM can achieve higher performance in
document classification than the previous state-of-the-art methods with only 50
to 200 labelled instances, especially when documents are long. Also, as an
extra benefit of HBM, the salient sentences identified by learned HBM are
useful as explanations for labelling documents based on a user study.
