  Current pre-training works in natural language generation pay little
attention to the problem of exposure bias on downstream tasks. To address this
issue, we propose an enhanced multi-flow sequence to sequence pre-training and
fine-tuning framework named ERNIE-GEN, which bridges the discrepancy between
training and inference with an infilling generation mechanism and a noise-aware
generation method. To make generation closer to human writing patterns, this
framework introduces a span-by-span generation flow that trains the model to
predict semantically-complete spans consecutively rather than predicting word
by word. Unlike existing pre-training methods, ERNIE-GEN incorporates
multi-granularity target sampling to construct pre-training data, which
enhances the correlation between encoder and decoder. Experimental results
demonstrate that ERNIE-GEN achieves state-of-the-art results with a much
smaller amount of pre-training data and parameters on a range of language
generation tasks, including abstractive summarization (Gigaword and
CNN/DailyMail), question generation (SQuAD), dialogue generation (Persona-Chat)
and generative question answering (CoQA).
