  We report our ongoing work about a new deep architecture working in tandem
with a statistical test procedure for jointly training texts and their label
descriptions for multi-label and multi-class classification tasks. A
statistical hypothesis testing method is used to extract the most informative
words for each given class. These words are used as a class description for
more label-aware text classification. Intuition is to help the model to
concentrate on more informative words rather than more frequent ones. The model
leverages the use of label descriptions in addition to the input text to
enhance text classification performance. Our method is entirely data-driven,
has no dependency on other sources of information than the training data, and
is adaptable to different classification problems by providing appropriate
training data without major hyper-parameter tuning. We trained and tested our
system on several publicly available datasets, where we managed to improve the
state-of-the-art on one set with a high margin, and to obtain competitive
results on all other ones.
