Abstract
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