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

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 evaluati...

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Veröffentlicht in:PloS one Jg. 17; H. 7; S. e0270308
Hauptverfasser: Li, Hanhui, Deng, Jie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 13.07.2022
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Analysis
Back propagation
Biology and Life Sciences
Computer and Information Sciences
Deep Learning
Engineering and Technology
Evaluation
Expected values
Feature extraction
Imports
Information retrieval
Innovations
Language
Machine learning
Machine translation
Methods
Modelling
Neural networks
Neurons
Propagation
Quality assessment
Quality control
Sentences
Social Sciences
Technology
Technology assessment
Translating
Translating and interpreting
Translation
Translations
Words (language)
China
ISSN:1932-6203, 1932-6203
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Zusammenfassung: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 articles into Bilingual words and optimizes the extraction effect of language vector features. Meantime, the translation language vector feature is introduced into the automatic DL-based TQA. The experimental findings corroborate that when the number of sentences increases, the number of actual translation errors and the evaluation scores of the proposed model increase, but the Bilingual Evaluation Understudy (BLEU) score is not significantly affected. When the number of sentences increases from 1,000 to 6,000, the BLEU increases from 96 to 98, which shows that the proposed model has good performance. Finally, the proposed model can realize the high-precision TQA of unreferenced English articles.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0270308