  Extreme multi-label text classification utilizes the label hierarchy to
partition extreme labels into multiple label groups, turning the task into
simple multi-group multi-label classification tasks. Current research encodes
labels as a vector with fixed length which needs establish multiple classifiers
for different label groups. The problem is how to build only one classifier
without sacrificing the label relationship in the hierarchy. This paper adopts
the multi-answer questioning task for extreme multi-label classification. This
paper also proposes an auxiliary classification evaluation metric. This study
adopts the proposed method and the evaluation metric to the legal domain. The
utilization of legal Berts and the study on task distribution are discussed.
The experiment results show that the proposed hierarchy and multi-answer
questioning task can do extreme multi-label classification for EURLEX dataset.
And in minor/fine-tuning the multi-label classification task, the domain
adapted BERT models could not show apparent advantages in this experiment. The
method is also theoretically applicable to zero-shot learning.
