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Despite the success of multilingual sequence-to-sequence pretraining, mostexisting approaches rely on monolingual corpora, and do not make use of thestrong cross-lingual signal contained in parallel data. In this paper, wepresent PARADISE (PARAllel & Denoising Integration in SEquence-to-sequencemodels), which extends the conventional denoising objective used to train thesemodels by (i) replacing words in the noised sequence according to amultilingual dictionary, and (ii) predicting the reference translationaccording to a parallel corpus instead of recovering the original sequence. Ourexperiments on machine translation and cross-lingual natural language inferenceshow an average improvement of 2.0 BLEU points and 6.7 accuracy points fromintegrating parallel data into pretraining, respectively, obtaining resultsthat are competitive with several popular models at a fraction of theircomputational cost.

PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining