@inproceedings{chi-etal-2021-mt6,
title = "m{T}6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs",
author = "Chi, Zewen and
Dong, Li and
Ma, Shuming and
Huang, Shaohan and
Singhal, Saksham and
Mao, Xian-Ling and
Huang, Heyan and
Song, Xia and
Wei, Furu",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.125",
doi = "10.18653/v1/2021.emnlp-main.125",
pages = "1671--1683",
abstract = "Multilingual T5 pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). Specifically, we explore three cross-lingual text-to-text pre-training tasks, namely, machine translation, translation pair span corruption, and translation span corruption. In addition, we propose a partially non-autoregressive objective for text-to-text pre-training. We evaluate the methods on seven multilingual benchmark datasets, including sentence classification, named entity recognition, question answering, and abstractive summarization. Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5.",
}
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<abstract>Multilingual T5 pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). Specifically, we explore three cross-lingual text-to-text pre-training tasks, namely, machine translation, translation pair span corruption, and translation span corruption. In addition, we propose a partially non-autoregressive objective for text-to-text pre-training. We evaluate the methods on seven multilingual benchmark datasets, including sentence classification, named entity recognition, question answering, and abstractive summarization. Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5.</abstract>
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%0 Conference Proceedings
%T mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
%A Chi, Zewen
%A Dong, Li
%A Ma, Shuming
%A Huang, Shaohan
%A Singhal, Saksham
%A Mao, Xian-Ling
%A Huang, Heyan
%A Song, Xia
%A Wei, Furu
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F chi-etal-2021-mt6
%X Multilingual T5 pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). Specifically, we explore three cross-lingual text-to-text pre-training tasks, namely, machine translation, translation pair span corruption, and translation span corruption. In addition, we propose a partially non-autoregressive objective for text-to-text pre-training. We evaluate the methods on seven multilingual benchmark datasets, including sentence classification, named entity recognition, question answering, and abstractive summarization. Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5.
%R 10.18653/v1/2021.emnlp-main.125
%U https://aclanthology.org/2021.emnlp-main.125
%U https://doi.org/10.18653/v1/2021.emnlp-main.125
%P 1671-1683
Markdown (Informal)
[mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs](https://aclanthology.org/2021.emnlp-main.125) (Chi et al., EMNLP 2021)
ACL
- Zewen Chi, Li Dong, Shuming Ma, Shaohan Huang, Saksham Singhal, Xian-Ling Mao, Heyan Huang, Xia Song, and Furu Wei. 2021. mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1671–1683, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.