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In this paper, we take the advantage of previous pre-trained models (PTMs)and propose a novel Chinese Pre-trained Unbalanced Transformer (CPT). Differentfrom previous Chinese PTMs, CPT is designed to utilize the shared knowledgebetween natural language understanding (NLU) and natural language generation(NLG) to boost the performance. CPT consists of three parts: a shared encoder,an understanding decoder, and a generation decoder. Two specific decoders witha shared encoder are pre-trained with masked language modeling (MLM) anddenoising auto-encoding (DAE) tasks, respectively. With the partially sharedarchitecture and multi-task pre-training, CPT can (1) learn specific knowledgeof both NLU or NLG tasks with two decoders and (2) be fine-tuned flexibly thatfully exploits the potential of the model. Moreover, the unbalanced Transformersaves the computational and storage cost, which makes CPT competitive andgreatly accelerates the inference of text generation. Experimental results on awide range of Chinese NLU and NLG tasks show the effectiveness of CPT.

CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation