Stochastic tokenization with BPE dropout during both pretraining and fine-tuning outperforms deterministic tokenization or fine-tuning-only dropout on low-resource NLP tasks.
Fully character-level neural machine translation without explicit segmentation.Transactions of the Associ- ation for Computational Linguistics, 5:365–378
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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.