Contrastive Bootstrapping for Label Refinement
- URL: http://arxiv.org/abs/2306.04544v1
- Date: Wed, 7 Jun 2023 15:49:04 GMT
- Title: Contrastive Bootstrapping for Label Refinement
- Authors: Shudi Hou, Yu Xia, Muhao Chen, Sujian Li
- Abstract summary: We propose a lightweight contrastive clustering-based bootstrapping method to iteratively refine the labels of passages.
Experiments on NYT and 20News show that our method outperforms the state-of-the-art methods by a large margin.
- Score: 34.55195008779178
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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