Cluster-Guided Label Generation in Extreme Multi-Label Classification

TL;DR

This paper introduces XLGen, a novel label generation approach for extreme multi-label classification that leverages label clustering and pre-trained models to improve tail label performance and generate plausible unseen labels.

Contribution

It proposes a cluster-guided label generation framework for XMC using pre-trained text-to-text models, addressing tail label performance and label relation modeling.

Findings

XLGen outperforms classification and generation baselines on tail labels.

The method improves overall performance on four XMC benchmarks.

Human evaluation shows XLGen generates plausible unseen labels.

Abstract

For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treating "Wikipedia" and "Wiki" as independent and separate labels. In this paper, we cast XMC as a generation task (XLGen), where we benefit from pre-trained text-to-text models. However, generating labels from the extremely large label space is challenging without any constraints or guidance. We, therefore, propose to guide label generation using label cluster information to hierarchically generate lower-level labels. We also find that frequency-based label ordering and using decoding ensemble methods are critical factors for the improvements in XLGen. XLGen with cluster guidance significantly outperforms the classification and generation baselines on tail labels, and also generally improves the overall performance in four popular XMC benchmarks. In human evaluation, we also find XLGen generates unseen but plausible labels. Our code is now available at https://github.com/alexa/xlgen-eacl-2023.