Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification
Ke Ji Yixin Lian Jingsheng Gao Baoyuan Wang
Due to the complex label hierarchy and intensive labeling cost in practice,
the hierarchical text classification (HTC) suffers a poor performance
especially when low-resource or few-shot settings are considered. Recently,
there is a growing trend of applying prompts on pre-trained language models
(PLMs), which has exhibited effectiveness in the few-shot flat text
classification tasks. However, limited work has studied the paradigm of
prompt-based learning in the HTC problem when the training data is extremely
scarce. In this work, we define a path-based few-shot setting and establish a
strict path-based evaluation metric to further explore few-shot HTC tasks. To
address the issue, we propose the hierarchical verbalizer ("HierVerb"), a
multi-verbalizer framework treating HTC as a single- or multi-label
classification problem at multiple layers and learning vectors as verbalizers
constrained by hierarchical structure and hierarchical contrastive learning. In
this manner, HierVerb fuses label hierarchy knowledge into verbalizers and
remarkably outperforms those who inject hierarchy through graph encoders,
maximizing the benefits of PLMs. Extensive experiments on three popular HTC
datasets under the few-shot settings demonstrate that prompt with HierVerb
significantly boosts the HTC performance, meanwhile indicating an elegant way
to bridge the gap between the large pre-trained model and downstream
hierarchical classification tasks. Our code and few-shot dataset are publicly
available at https://github.com/1KE-JI/HierVerb.