  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 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.
