mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer
Linting Xue,Noah Constant,Adam Roberts,Mihir Kale,Rami Al-Rfou,Aditya Siddhant,Aditya Barua,Colin Raffel +7 more
- pp 483-498
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TLDR
This paper proposed a multilingual variant of T5, mT5, which was pre-trained on a new Common Crawl-based dataset covering 101 languages and achieved state-of-the-art performance on many multilingual benchmarks.Abstract:
The recent “Text-to-Text Transfer Transformer” (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent “accidental translation” in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model checkpoints used in this work are publicly available.read more
Citations
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Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?
En-Shiun Annie Lee,Sarubi Thillainathan,Shravan Nayak,Surangika Ranathunga,David Ifeoluwa Adelani,Ruisi Su,Arya D. McCarthy +6 more
TL;DR: While mBART is robust to domain differences, its translations for unseen and typologically distant languages remain below 3.0 BLEU; it is suggested that the emphasis should be shifted from new models to new data.
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mFACE: Multilingual Summarization with Factual Consistency Evaluation
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Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation
TL;DR: This work explores multiple approaches for leveraging much more widely available unsupervised and weakly-supervised speech and text data to improve the performance of direct S2ST based on Translatotron 2.
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QAmeleon: Multilingual QA with Only 5 Examples
Priyanka Agrawal,Chris Alberti,Fantine Huot,Joshua Maynez,Ji Ma,Sebastian Ruder,Kuzman Ganchev,Dipanjan Das,Mirella Lapata +8 more
TL;DR: The authors use a pre-trained language model under a few-shot learning setting to automatically generate multilingual data upon which QA models are trained, thus avoiding costly annotation, which is a viable alternative to large-scale annotation.
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