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Title: Contrastive Bootstrapping for Label Refinement
Traditional text classification typically categorizes texts into pre-defined coarse-grained classes, from which the produced models cannot handle the real-world scenario where finer categories emerge periodically for accurate services. In this work, we investigate the setting where fine-grained classification is done only using the annotation of coarse-grained categories and the coarse-to-fine mapping. We propose a lightweight contrastive clustering-based bootstrapping method to iteratively refine the labels of passages. During clustering, it pulls away negative passage-prototype pairs under the guidance of the mapping from both global and local perspectives. Experiments on NYT and 20News show that our method outperforms the state-of-the-art methods by a large margin.  more » « less
Award ID(s):
2105329
PAR ID:
10440669
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
Page Range / eLocation ID:
976 to 985
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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