SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
This addresses the problem of classifying into many classes with limited supervision for applications like news and e-commerce, representing an incremental improvement.
The paper tackled zero-shot and few-shot extreme classification by developing SemSup-XC, which uses semantic class descriptions and a hybrid matching module, achieving state-of-the-art performance with gains of up to 12 precision points on zero-shot and over 10 precision points on one-shot tests across three datasets.
Extreme classification (XC) involves predicting over large numbers of classes (thousands to millions), with real-world applications like news article classification and e-commerce product tagging. The zero-shot version of this task requires generalization to novel classes without additional supervision. In this paper, we develop SemSup-XC, a model that achieves state-of-the-art zero-shot and few-shot performance on three XC datasets derived from legal, e-commerce, and Wikipedia data. To develop SemSup-XC, we use automatically collected semantic class descriptions to represent classes and facilitate generalization through a novel hybrid matching module that matches input instances to class descriptions using a combination of semantic and lexical similarity. Trained with contrastive learning, SemSup-XC significantly outperforms baselines and establishes state-of-the-art performance on all three datasets considered, gaining up to 12 precision points on zero-shot and more than 10 precision points on one-shot tests, with similar gains for recall@10. Our ablation studies highlight the relative importance of our hybrid matching module and automatically collected class descriptions.