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

TL;DR

CPT is a novel Chinese pre-trained unbalanced Transformer that effectively combines understanding and generation tasks, improving performance and efficiency through shared architecture and multi-task pre-training.

Contribution

The paper introduces CPT, a unbalanced Transformer with shared encoder and task-specific decoders, enabling efficient multi-task learning for Chinese NLP tasks.

Findings

Outperforms previous models on Chinese NLU and NLG tasks

Reduces computational and storage costs

Accelerates inference speed for text generation

Abstract

In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a novel Chinese Pre-trained Unbalanced Transformer (CPT). Different from previous Chinese PTMs, CPT is designed to utilize the shared knowledge between 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 with a shared encoder are pre-trained with masked language modeling (MLM) and denoising auto-encoding (DAE) tasks, respectively. With the partially shared architecture and multi-task pre-training, CPT can (1) learn specific knowledge of both NLU or NLG tasks with two decoders and (2) be fine-tuned flexibly that fully exploits the potential of the model. Moreover, the unbalanced Transformer saves the computational and storage cost, which makes CPT competitive and greatly accelerates the inference of text generation. Experimental results on a wide range of Chinese NLU and NLG tasks show the effectiveness of CPT.