Hierarchical Multi-Label Generation with Probabilistic Level-Constraint

TL;DR

This paper introduces a generative approach with probabilistic constraints for hierarchical multi-label generation, achieving state-of-the-art results and precise control over output in complex taxonomy-based label prediction.

Contribution

It redefines hierarchical multi-label classification as a generation task and employs probabilistic level constraints to improve accuracy and control without preliminary clustering.

Findings

Achieves new SOTA performance in HMG tasks

Provides precise control over label count, length, and levels

Outperforms previous methods in constrained output quality

Abstract

Hierarchical Extreme Multi-Label Classification poses greater difficulties compared to traditional multi-label classification because of the intricate hierarchical connections of labels within a domain-specific taxonomy and the substantial number of labels. Some of the prior research endeavors centered on classifying text through several ancillary stages such as the cluster algorithm and multiphase classification. Others made attempts to leverage the assistance of generative methods yet were unable to properly control the output of the generative model. We redefine the task from hierarchical multi-Label classification to Hierarchical Multi-Label Generation (HMG) and employ a generative framework with Probabilistic Level Constraints (PLC) to generate hierarchical labels within a specific taxonomy that have complex hierarchical relationships. The approach we proposed in this paper enables the framework to generate all relevant labels across levels for each document without relying on preliminary operations like clustering. Meanwhile, it can control the model output precisely in terms of count, length, and level aspects. Experiments demonstrate that our approach not only achieves a new SOTA performance in the HMG task, but also has a much better performance in constrained the output of model than previous research work.