mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

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Abstract

Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

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Linting Xue

Google (United States)

Noah Constant

Google (United States)

Adam P. Roberts

Liverpool School of Tropical Medicine

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Google (United States)

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Xue et al. (Fri,) studied this question.

synapsesocial.com/papers/69d84d9f8c03fbaff8beee50 — DOI: https://doi.org/10.18653/v1/2021.naacl-main.41

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Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Pre-training via Paraphrasing· 2020 · 56 citations
SQuAD: 100,000+ Questions for Machine Comprehension of Text· 2016 · 6,313 citations
XNLI: Evaluating Cross-lingual Sentence Representations· 2018 · 932 citations
Unifying Question Answering and Text Classification via Span Extraction.· 2019 · 24 citations
BERTje: A Dutch BERT Model· 2019 · 215 citations

mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

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