  We propose a novel model for multi-label text classification, which is based
on sequence-to-sequence learning. The model generates higher-level semantic
unit representations with multi-level dilated convolution as well as a
corresponding hybrid attention mechanism that extracts both the information at
the word-level and the level of the semantic unit. Our designed dilated
convolution effectively reduces dimension and supports an exponential expansion
of receptive fields without loss of local information, and the
attention-over-attention mechanism is able to capture more summary relevant
information from the source context. Results of our experiments show that the
proposed model has significant advantages over the baseline models on the
dataset RCV1-V2 and Ren-CECps, and our analysis demonstrates that our model is
competitive to the deterministic hierarchical models and it is more robust to
classifying low-frequency labels.
