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

arXiv: https://arxiv.org/abs/2303.01064 · HTML: https://mlsanity.com/paper/2303.01064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [LEGAL-BERT: The Muppets straight out of Law School](https://mlsanity.com/paper/2010.02559/) (2020)
- [Large-Scale Multi-Label Text Classification on EU Legislation](https://mlsanity.com/paper/1906.02192/) (2019)
- [What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning](https://mlsanity.com/paper/1911.03090/) (2019)
- [AEDA: An Easier Data Augmentation Technique for Text Classification](https://mlsanity.com/paper/2108.13230/) (2021)
- [A Simple and Effective Model for Answering Multi-span Questions](https://mlsanity.com/paper/1909.13375/) (2019)

## Abstract (arXiv)

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.