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Extreme multi-label text classification utilizes the label hierarchy topartition extreme labels into multiple label groups, turning the task intosimple multi-group multi-label classification tasks. Current research encodeslabels as a vector with fixed length which needs establish multiple classifiersfor different label groups. The problem is how to build only one classifierwithout sacrificing the label relationship in the hierarchy. This paper adoptsthe multi-answer questioning task for extreme multi-label classification. Thispaper also proposes an auxiliary classification evaluation metric. This studyadopts the proposed method and the evaluation metric to the legal domain. Theutilization of legal Berts and the study on task distribution are discussed.The experiment results show that the proposed hierarchy and multi-answerquestioning task can do extreme multi-label classification for EURLEX dataset.And in minor/fine-tuning the multi-label classification task, the domainadapted BERT models could not show apparent advantages in this experiment. Themethod is also theoretically applicable to zero-shot learning.

Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy