  In this paper, we transform tag recommendation into a word-based text
generation problem and introduce a sequence-to-sequence model. The model
inherits the advantages of LSTM-based encoder for sequential modeling and
attention-based decoder with local positional encodings for learning relations
globally. Experimental results on Zhihu datasets illustrate the proposed model
outperforms other state-of-the-art text classification based methods.
