mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences

• URL: http://arxiv.org/abs/2305.11129v2
• Date: Thu, 26 Oct 2023 22:43:26 GMT
• Title: mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences
• Authors: David Uthus, Santiago Onta\~n\'on, Joshua Ainslie, Mandy Guo
• Abstract summary: This model builds upon the architecture of LongT5, while leveraging the multilingual datasets used for pretraining mT5 and the pretraining tasks of UL2. We evaluate this model on a variety of multilingual summarization and question-answering tasks, and the results show stronger performance for mLongT5 when compared to existing multilingual models such as mBART or M-BERT.
• Score: 17.461172187276734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present our work on developing a multilingual, efficient text-to-text transformer that is suitable for handling long inputs. This model, called mLongT5, builds upon the architecture of LongT5, while leveraging the multilingual datasets used for pretraining mT5 and the pretraining tasks of UL2. We evaluate this model on a variety of multilingual summarization and question-answering tasks, and the results show stronger performance for mLongT5 when compared to existing multilingual models such as mBART or M-BERT.

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