Stochastic tokenization with BPE dropout during both pretraining and fine-tuning outperforms deterministic tokenization or fine-tuning-only dropout on low-resource NLP tasks.
[Cognettaet al., 2024 ] Marco Cognetta, Vil ´em Zouhar, and Naoaki Okazaki
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Pretraining Language Models with Subword Regularization: An Empirical Study of BPE Dropout in Low-Resource NLP
Stochastic tokenization with BPE dropout during both pretraining and fine-tuning outperforms deterministic tokenization or fine-tuning-only dropout on low-resource NLP tasks.