Papers by Hugo Abonizio

1 papers
MonoByte: A Pool of Monolingual Byte-level Language Models (2022.coling-1)

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Challenge: Existing studies have shown that multilingual models can achieve zero-shot cross-lingual performance on various NLP tasks, but due to the cost of pretraining, they often use public models with limited budgets.
Approach: They propose to use tokenized models to test cross-lingual ability in multilingual and monolingual corpora.
Outcome: The results show that models pretrained on multilingual and even monolingual corpora perform better than models pre-trained on SOTA models.

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