Pretrained Language Model for Text Generation: A Survey

Pretrained Language Model for Text Generation: A Survey

Junyi Li, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Survey Track. Pages 4492-4499. https://doi.org/10.24963/ijcai.2021/612

Text generation has become one of the most important yet challenging tasks in natural language processing (NLP). The resurgence of deep learning has greatly advanced this field by neural generation models, especially the paradigm of pretrained language models (PLMs). In this paper, we present an overview of the major advances achieved in the topic of PLMs for text generation. As the preliminaries, we present the general task definition and briefly describe the mainstream architectures of PLMs for text generation. As the core content, we discuss how to adapt existing PLMs to model different input data and satisfy special properties in the generated text. We further summarize several important fine-tuning strategies for text generation. Finally, we present several future directions and conclude this paper. Our survey aims to provide text generation researchers a synthesis and pointer to related research.
Keywords:
Natural language processing: General