Optimization of Translation Techniques between English and Chinese Literary Works in the Information Age
Under the background of cultural globalization, English-Chinese literary translation plays an important role in which it is not only a process of language conversion, but also a process of cultural conversion. In this paper, the BiGUR-LM-Attention optimization model is fused and constructed using th...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns.2023.2.01701 |
| _version_ | 1797340471596417024 |
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| author | Li Jun |
| author_facet | Li Jun |
| author_sort | Li Jun |
| collection | DOAJ |
| description | Under the background of cultural globalization, English-Chinese literary translation plays an important role in which it is not only a process of language conversion, but also a process of cultural conversion. In this paper, the BiGUR-LM-Attention optimization model is fused and constructed using the WordNet semantic similarity model, GRU-LM one-way gated similarity model, and BiGR-LM two-way gated similarity model. The LDA theme model is selected to generate the 3-layer Bayesian network structure of literary works’ paragraphs, themes and words to obtain the probability information that represents the highest attention of the work’s text theme, which constitutes the attention mechanism feature word vector. Finally, five classic literary works are selected as the training corpus to compare and analyze the translation quality between machine translation and human translation in the mutual translation of English and Chinese literary works. The results show that the number of errors and the total score of machine translation are 95 and 275, which are significantly lower than those of manual translation, 105.37 and 360.19. The new model has outstanding translation performance in semantic recognition, dialect, and special nouns, which effectively improves the translation quality of literary works and is of great significance for the dissemination of cultural works. |
| first_indexed | 2024-03-08T10:04:08Z |
| format | Article |
| id | doaj.art-3a2eb155e82d4cf5a5c2991a89287ebe |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:04:08Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-3a2eb155e82d4cf5a5c2991a89287ebe2024-01-29T08:52:45ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.01701Optimization of Translation Techniques between English and Chinese Literary Works in the Information AgeLi Jun01School of Foreign Languages and International Education, Jiangxi University of Technology; Nanchang, Jiangxi, 330098, China.Under the background of cultural globalization, English-Chinese literary translation plays an important role in which it is not only a process of language conversion, but also a process of cultural conversion. In this paper, the BiGUR-LM-Attention optimization model is fused and constructed using the WordNet semantic similarity model, GRU-LM one-way gated similarity model, and BiGR-LM two-way gated similarity model. The LDA theme model is selected to generate the 3-layer Bayesian network structure of literary works’ paragraphs, themes and words to obtain the probability information that represents the highest attention of the work’s text theme, which constitutes the attention mechanism feature word vector. Finally, five classic literary works are selected as the training corpus to compare and analyze the translation quality between machine translation and human translation in the mutual translation of English and Chinese literary works. The results show that the number of errors and the total score of machine translation are 95 and 275, which are significantly lower than those of manual translation, 105.37 and 360.19. The new model has outstanding translation performance in semantic recognition, dialect, and special nouns, which effectively improves the translation quality of literary works and is of great significance for the dissemination of cultural works.https://doi.org/10.2478/amns.2023.2.01701bigur-lm-attentionattention mechanismfeature word vectormachine translationliterary works49n30 |
| spellingShingle | Li Jun Optimization of Translation Techniques between English and Chinese Literary Works in the Information Age Applied Mathematics and Nonlinear Sciences bigur-lm-attention attention mechanism feature word vector machine translation literary works 49n30 |
| title | Optimization of Translation Techniques between English and Chinese Literary Works in the Information Age |
| title_full | Optimization of Translation Techniques between English and Chinese Literary Works in the Information Age |
| title_fullStr | Optimization of Translation Techniques between English and Chinese Literary Works in the Information Age |
| title_full_unstemmed | Optimization of Translation Techniques between English and Chinese Literary Works in the Information Age |
| title_short | Optimization of Translation Techniques between English and Chinese Literary Works in the Information Age |
| title_sort | optimization of translation techniques between english and chinese literary works in the information age |
| topic | bigur-lm-attention attention mechanism feature word vector machine translation literary works 49n30 |
| url | https://doi.org/10.2478/amns.2023.2.01701 |
| work_keys_str_mv | AT lijun optimizationoftranslationtechniquesbetweenenglishandchineseliteraryworksintheinformationage |