  Multi-label text classification (MLTC) aims to annotate documents with the
most relevant labels from a number of candidate labels. In real applications,
the distribution of label frequency often exhibits a long tail, i.e., a few
labels are associated with a large number of documents (a.k.a. head labels),
while a large fraction of labels are associated with a small number of
documents (a.k.a. tail labels). To address the challenge of insufficient
training data on tail label classification, we propose a Head-to-Tail Network
(HTTN) to transfer the meta-knowledge from the data-rich head labels to
data-poor tail labels. The meta-knowledge is the mapping from few-shot network
parameters to many-shot network parameters, which aims to promote the
generalizability of tail classifiers. Extensive experimental results on three
benchmark datasets demonstrate that HTTN consistently outperforms the
state-of-the-art methods. The code and hyper-parameter settings are released
for reproducibility
