{"id":782833,"date":"2021-10-07T11:54:12","date_gmt":"2021-10-07T18:54:12","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=782833"},"modified":"2021-10-07T11:54:12","modified_gmt":"2021-10-07T18:54:12","slug":"mt6-multilingual-pretrained-text-to-text-transformer-with-translation-pairs","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mt6-multilingual-pretrained-text-to-text-transformer-with-translation-pairs\/","title":{"rendered":"MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs"},"content":{"rendered":"
Multilingual T5 (mT5) 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 eight 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
Multilingual T5 (mT5) 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 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