A Survey of Pretrained Language Models Based Text Generation
- URL: http://arxiv.org/abs/2201.05273v1
- Date: Fri, 14 Jan 2022 01:44:58 GMT
- Title: A Survey of Pretrained Language Models Based Text Generation
- Authors: Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie and Ji-Rong Wen
- Abstract summary: Text Generation aims to produce plausible and readable text in human language from input data.
Deep learning has greatly advanced this field by neural generation models, especially the paradigm of pretrained language models (PLMs)
Grounding text generation on PLMs is seen as a promising direction in both academia and industry.
- Score: 97.64625999380425
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Text Generation aims to produce plausible and readable text in human language
from input data. The resurgence of deep learning has greatly advanced this
field by neural generation models, especially the paradigm of pretrained
language models (PLMs). Grounding text generation on PLMs is seen as a
promising direction in both academia and industry. In this survey, we present
the recent advances achieved in the topic of PLMs for text generation. In
detail, we begin with introducing three key points of applying PLMs to text
generation: 1) how to encode the input data as representations preserving input
semantics which can be fused into PLMs; 2) how to design a universal and
performant architecture of PLMs served as generation models; and 3) how to
optimize PLMs given the reference text and ensure the generated text satisfying
special text properties. Then, we figure out several challenges and future
directions within each key point. Next, we present a summary of various useful
resources and typical text generation applications to work with PLMs. Finally,
we conclude and summarize the contribution of this survey.
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