Automatic hashtag annotation plays an important role in content understanding
for microblog posts. To date, progress made in this field has been restricted
to phrase selection from limited candidates, or word-level hashtag discovery
using topic models. Different from previous work considering hashtags to be
inseparable, our work is the first effort to annotate hashtags with a novel
sequence generation framework via viewing the hashtag as a short sequence of
words. Moreover, to address the data sparsity issue in processing short
microblog posts, we propose to jointly model the target posts and the
conversation contexts initiated by them with bidirectional attention. Extensive
experimental results on two large-scale datasets, newly collected from English
Twitter and Chinese Weibo, show that our model significantly outperforms
state-of-the-art models based on classification. Further studies demonstrate
our ability to effectively generate rare and even unseen hashtags, which is
however not possible for most existing methods.

本文提出了一种新的序列生成框架方法，通过将 hashtag 视为简短的单词序列来自动标注微博帖子的 hashtag，并提出了一个双向关注的方法来一起处理目标文章和由它们引发的对话内容。实验结果表明，我们的模型显著优于基于分类的现有模型，并能够有效生成罕见甚至未见过的 hashtag。