Stochastic tokenization with BPE dropout during both pretraining and fine-tuning outperforms deterministic tokenization or fine-tuning-only dropout on low-resource NLP tasks.
Morphynet: A large multilin- gual database of derivational and inflectional morphol- ogy
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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.