Document detail
ID
oai:pubmedcentral.nih.gov:9482...
Topic
Research ArticleAuthor
Liu, Lei Sun, Yeguo Liu, Yihong Roxas, Rachel Edita O. Raga, Rodolfo C.Langue
enEditor
Hindawi
Category
Computational Intelligence and Neuroscience
Year
2022
listing date
12/12/2022
Keywords
solutions textAbstract
Text generation has always been limited by the lack of corpus data required for language model (LM) training and the low quality of the generated text.
Researchers have proposed some solutions, but these solutions are often complex and will greatly increase the consumption of computing resources.
Referring to the current main solutions, this paper proposes a lightweight language model (EDA-BoB) based on text augmentation technology and knowledge understanding mechanism.
Experiments show that the EDA-BoB model cannot only expand the scale of the training data set but also ensure the data quality at the cost of consuming little computing resources.
Moreover, our model is shown to combine the contextual semantics of sentences to generate rich and accurate texts.
Liu, Lei,Sun, Yeguo,Liu, Yihong,Roxas, Rachel Edita O.,Raga, Rodolfo C., 2022, Research and Implementation of Text Generation Based on Text Augmentation and Knowledge Understanding, Hindawi
A Novel MR Imaging Sequence of 3D-ZOOMit Real Inversion-Recovery Imaging Improves Endolymphatic Hydrops Detection in Patients with Ménière Disease
ménière disease p < detection imaging sequences 3d-zoomit 3d endolymphatic real tse reconstruction ir inversion-recovery hydrops ratio
