  We explore solutions for automated labeling of content in bug trackers and
customer support systems. In order to do that, we classify content in terms of
several criteria, such as priority or product area. In the first part of the
paper, we provide an overview of existing methods used for text classification.
These methods fall into two categories - the ones that rely on neural networks
and the ones that don't. We evaluate results of several solutions of both
kinds. In the second part of the paper we present our own recurrent neural
network solution based on hierarchical attention paradigm. It consists of
several Hierarchical Attention network blocks with varying Gated Recurrent Unit
cell sizes and a complementary shallow network that goes alongside. Lastly, we
evaluate above-mentioned methods when predicting fields from two datasets -
Arch Linux bug tracker and Chromium bug tracker. Our contributions include a
comprehensive benchmark between a variety of methods on relevant datasets; a
novel solution that outperforms previous generation methods; and two new
datasets that are made public for further research.
