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

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arxiv 2305.11129 v2 pith:VYDFV6FN submitted 2023-05-18 cs.CL

David Uthus , Santiago Ontañón , Joshua Ainslie , Mandy Guo This is my paper
classification cs.CL
keywords multilingualmlongt5efficientmodelpretrainingtaskstext-to-texttransformer
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Role of Orthographic Consistency in Multilingual Embedding Models for Text Classification in Arabic-Script Languages

    cs.CL 2025-07 reject novelty 4.0 of 10

    Language-specific RoBERTa models for four Arabic-script languages beat multilingual baselines on news classification, though the claimed orthographic-consistency mechanism is not demonstrated.

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