  Text classification, a core component of task-oriented dialogue systems,
attracts continuous research from both the research and industry community, and
has resulted in tremendous progress. However, existing method does not consider
the use of label information, which may weaken the performance of text
classification systems in some token-aware scenarios. To address the problem,
in this paper, we introduce the use of label information as label embedding for
the task of text classification and achieve remarkable performance on benchmark
dataset.
