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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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Detalles Bibliográficos
Autores principales: Li, Hanhui, Deng, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278734/
https://www.ncbi.nlm.nih.gov/pubmed/35830434
http://dx.doi.org/10.1371/journal.pone.0270308
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author Li, Hanhui
Deng, Jie
author_facet Li, Hanhui
Deng, Jie
author_sort Li, Hanhui
collection PubMed
description 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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spelling pubmed-92787342022-07-14 Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning Li, Hanhui Deng, Jie PLoS One Research Article 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. Public Library of Science 2022-07-13 /pmc/articles/PMC9278734/ /pubmed/35830434 http://dx.doi.org/10.1371/journal.pone.0270308 Text en © 2022 Li, Deng https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Li, Hanhui
Deng, Jie
Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning
title Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning
title_full Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning
title_fullStr Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning
title_full_unstemmed Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning
title_short Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning
title_sort unreferenced english articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278734/
https://www.ncbi.nlm.nih.gov/pubmed/35830434
http://dx.doi.org/10.1371/journal.pone.0270308
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