  Text classification has become increasingly challenging due to the continuous
refinement of classification label granularity and the expansion of
classification label scale. To address that, some research has been applied
onto strategies that exploit the hierarchical structure in problems with a
large number of categories. At present, hierarchical text classification (HTC)
has received extensive attention and has broad application prospects. Making
full use of the relationship between parent category and child category in text
classification task can greatly improve the performance of classification. In
this paper, We propose a joint embedding of text and parent category based on
hierarchical fine-tuning ordered neurons LSTM (HFT-ONLSTM) for HTC. Our method
makes full use of the connection between the upper-level and lower-level
labels. Experiments show that our model outperforms the state-of-the-art
hierarchical model at a lower computation cost.
