Multi-label classification is an important yet challenging task in natural
language processing. It is more complex than single-label classification in
that the labels tend to be correlated. Existing methods tend to ignore the
correlations between labels. Besides, different parts of the text can
contribute differently for predicting different labels, which is not considered
by existing models. In this paper, we propose to view the multi-label
classification task as a sequence generation problem, and apply a sequence
generation model with a novel decoder structure to solve it. Extensive
experimental results show that our proposed methods outperform previous work by
a substantial margin. Further analysis of experimental results demonstrates
that the proposed methods not only capture the correlations between labels, but
also select the most informative words automatically when predicting different
labels.

本文提出将多标签分类任务视为序列生成问题，并应用具有新型解码器结构的序列生成模型来解决这个任务，实验结果表明我们的建议方法大大优于以前的工作，同时分析实验结果表明建议方法不仅捕捉标签之间的相关性，而且在预测不同标签时自动选择最有信息的词。