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

David Uthus, Santiago Ontanon, Joshua Ainslie, Mandy Guo

Submission Type: Regular Short Paper

Submission Track: Multilinguality and Linguistic Diversity

Submission Track 2: Efficient Methods for NLP

Keywords: Multilinguality, Efficient model, Long inputs

TL;DR: We present a multilingual pretrained model that can efficiently handle long inputs.

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.

Submission Number: 209