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    <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.</abstract>
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%0 Conference Proceedings
%T mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences
%A Uthus, David
%A Ontanon, Santiago
%A Ainslie, Joshua
%A Guo, Mandy
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F uthus-etal-2023-mlongt5
%X 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.
%R 10.18653/v1/2023.findings-emnlp.628
%U https://aclanthology.org/2023.findings-emnlp.628/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.628
%P 9380-9386

Markdown (Informal)

[mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences](https://aclanthology.org/2023.findings-emnlp.628/) (Uthus et al., Findings 2023)

• mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences (Uthus et al., Findings 2023)

ACL

• David Uthus, Santiago Ontanon, Joshua Ainslie, and Mandy Guo. 2023. mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9380–9386, Singapore. Association for Computational Linguistics.