SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
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DOI：
10.48550/arxiv.2301.11309
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发表时间：
2023-01
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通讯作者：
Pranjal Aggarwal;A. Deshpande;Karthik Narasimhan
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作者：
Pranjal Aggarwal;A. Deshpande;Karthik Narasimhan
极端分类（XC）涉及预测大量课程（数千到数百万），并具有新闻文章分类和电子商务产品标签等现实应用程序。此任务的零击版本需要在没有其他监督的情况下对新颖类的概括。在本文中，我们开发了SEMSUP-XC，该模型在从法律，电子商务和Wikipedia数据中得出的三个XC数据集上实现了最先进的零拍摄和几乎没有发射的性能。为了开发SEMSUP-XC，我们使用自动收集的语义类描述来表示类，并通过一种新型混合匹配模块来促进概括，该模块将输入实例匹配到使用语义和词汇相似性的组合与类描述相匹配。经过对比度学习的培训，SEMSUP-XC显着胜过基线，并在考虑的所有三个数据集上建立了最先进的性能，在零照片上最多可获得12个精度点，并在一次性测试中获得10个以上的精度点，并具有10个以上的精度测试，并具有多达12个精度。召回@10的类似收益。我们的消融研究突出了混合匹配模块的相对重要性，并自动收集了类描述。
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.