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Title: Understanding The Robustness of Self-supervised Learning Through Topic Modeling
Self-supervised learning has signi ficantly improved the performance of many NLP tasks. In this paper, we highlight a key advantage of self-supervised learning - when applied to data generated by topic models, self-supervised learning can be oblivious to the specifi c model, and hence is less susceptible to model misspeci fication. In particular, we prove that commonly used self-supervised objectives based on reconstruction or contrastive samples can both recover useful posterior information for general topic models. Empirically, we show that the same objectives can perform competitively against posterior inference using the correct model, while outperforming posterior inference using misspecifi ed model.  more » « less
Award ID(s):
1704656 1845171
NSF-PAR ID:
10346978
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
International Conference on Learning Representations
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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