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While pretrained encoders have achieved success in various natural languageunderstanding (NLU) tasks, there is a gap between these pretrained encoders andnatural language generation (NLG). NLG tasks are often based on theencoder-decoder framework, where the pretrained encoders can only benefit partof it. To reduce this gap, we introduce DeltaLM, a pretrained multilingualencoder-decoder model that regards the decoder as the task layer ofoff-the-shelf pretrained encoders. Specifically, we augment the pretrainedmultilingual encoder with a decoder and pre-train it in a self-supervised way.To take advantage of both the large-scale monolingual data and bilingual data,we adopt the span corruption and translation span corruption as thepre-training tasks. Experiments show that DeltaLM outperforms various strongbaselines on both natural language generation and translation tasks, includingmachine translation, abstractive text summarization, data-to-text, and questiongeneration. The code and pretrained models are available at\url{https://aka.ms/deltalm}.

DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders