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.

本研究开发了 SemSup-XC 模型，利用混合匹配模块将输入实例与语义类别描述相匹配，通过对比学习，实现了在三个 XC 数据集上对零样本和少样本的最新成果，得分提升了 12 个精度点以上。