  Current text classification methods typically encode the text merely into
embedding before a naive or complicated classifier, which ignores the
suggestive information contained in the label text. As a matter of fact, humans
classify documents primarily based on the semantic meaning of the
subcategories. We propose a novel model structure via siamese BERT and
interactive double attentions named IDEA ( Interactive DoublE Attentions) to
capture the information exchange of text and label names. Interactive double
attentions enable the model to exploit the inter-class and intra-class
information from coarse to fine, which involves distinguishing among all labels
and matching the semantical subclasses of ground truth labels. Our proposed
method outperforms the state-of-the-art methods using label texts significantly
with more stable results.
