Unreferenced English articles' translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning.

ABSTRACT: Currently, both manual and automatic evaluation technology can evaluate the translation quality of unreferenced English articles, playing a particular role in detecting translation results. Still, their deficiency is the lack of a close or noticeable relationship between evaluation time and evaluation theory. Thereupon, to realize the automatic Translation Quality Assessment (TQA) of unreferenced English articles, this paper proposes an automatic TQA model based on Sparse AutoEncoder (SAE) under the background of Deep Learning (DL). Meanwhile, the DL-based information extraction method employs AutoEncoder (AE) in the bilingual words' unsupervised learning stage to reconstruct the translation language vector features. Then, it imports the translation information of unreferenced English arti

SUBMITTER: Li H 

PROVIDER: S-EPMC9278734 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature