Neural Machine Translation of Electrical Engineering Based on Vector Fusion Article Swipe
Hong Chen
,
Yuan Chen
,
Juwei Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13042325
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13042325
The development of neural machine translation has achieved a good translation effect on large-scale general corpora, but there are still many problems in the translation of low resources and specific fields. This paper studies the problem of machine translation in the field of electrical engineering and fuses the multi-layer vectors at the encoder side of the model. On this basis, the decoder unit of the translation model is improved, and a multi-attention mechanism translation model based on vector fusion is proposed, which improves the ability of the model to extract features and achieves a better translation effect on Chinese-English translation tasks. The experimental results show that the BLEU (bilingual evaluation understudy) value of the improved translation system in the field of electrical engineering has increased by 0.15–1.58 percentage points.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13042325
- https://www.mdpi.com/2076-3417/13/4/2325/pdf?version=1676351958
- OA Status
- gold
- Cited By
- 3
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4320496145
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4320496145Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app13042325Digital Object Identifier
- Title
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Neural Machine Translation of Electrical Engineering Based on Vector FusionWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
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2023-02-10Full publication date if available
- Authors
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Hong Chen, Yuan Chen, Juwei ZhangList of authors in order
- Landing page
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https://doi.org/10.3390/app13042325Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/13/4/2325/pdf?version=1676351958Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/13/4/2325/pdf?version=1676351958Direct OA link when available
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Machine translation, Translation (biology), Computer science, Artificial intelligence, Example-based machine translation, Natural language processing, Field (mathematics), Mathematics, Chemistry, Biochemistry, Gene, Pure mathematics, Messenger RNATop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.This | 31 |
| abstract_inverted_index.good | 9 |
| abstract_inverted_index.many | 20 |
| abstract_inverted_index.show | 104 |
| abstract_inverted_index.side | 53 |
| abstract_inverted_index.that | 105 |
| abstract_inverted_index.this | 58 |
| abstract_inverted_index.unit | 62 |
| abstract_inverted_index.based | 75 |
| abstract_inverted_index.field | 41, 119 |
| abstract_inverted_index.fuses | 46 |
| abstract_inverted_index.model | 66, 74, 87 |
| abstract_inverted_index.paper | 32 |
| abstract_inverted_index.still | 19 |
| abstract_inverted_index.there | 17 |
| abstract_inverted_index.value | 111 |
| abstract_inverted_index.which | 81 |
| abstract_inverted_index.basis, | 59 |
| abstract_inverted_index.better | 94 |
| abstract_inverted_index.effect | 11, 96 |
| abstract_inverted_index.fusion | 78 |
| abstract_inverted_index.model. | 56 |
| abstract_inverted_index.neural | 3 |
| abstract_inverted_index.system | 116 |
| abstract_inverted_index.tasks. | 100 |
| abstract_inverted_index.vector | 77 |
| abstract_inverted_index.ability | 84 |
| abstract_inverted_index.decoder | 61 |
| abstract_inverted_index.encoder | 52 |
| abstract_inverted_index.extract | 89 |
| abstract_inverted_index.fields. | 30 |
| abstract_inverted_index.general | 14 |
| abstract_inverted_index.machine | 4, 37 |
| abstract_inverted_index.points. | 128 |
| abstract_inverted_index.problem | 35 |
| abstract_inverted_index.results | 103 |
| abstract_inverted_index.studies | 33 |
| abstract_inverted_index.vectors | 49 |
| abstract_inverted_index.achieved | 7 |
| abstract_inverted_index.achieves | 92 |
| abstract_inverted_index.corpora, | 15 |
| abstract_inverted_index.features | 90 |
| abstract_inverted_index.improved | 114 |
| abstract_inverted_index.improves | 82 |
| abstract_inverted_index.problems | 21 |
| abstract_inverted_index.specific | 29 |
| abstract_inverted_index.improved, | 68 |
| abstract_inverted_index.increased | 124 |
| abstract_inverted_index.mechanism | 72 |
| abstract_inverted_index.proposed, | 80 |
| abstract_inverted_index.resources | 27 |
| abstract_inverted_index.(bilingual | 108 |
| abstract_inverted_index.electrical | 43, 121 |
| abstract_inverted_index.evaluation | 109 |
| abstract_inverted_index.percentage | 127 |
| abstract_inverted_index.0.15–1.58 | 126 |
| abstract_inverted_index.development | 1 |
| abstract_inverted_index.engineering | 44, 122 |
| abstract_inverted_index.large-scale | 13 |
| abstract_inverted_index.multi-layer | 48 |
| abstract_inverted_index.translation | 5, 10, 24, 38, 65, 73, 95, 99, 115 |
| abstract_inverted_index.understudy) | 110 |
| abstract_inverted_index.experimental | 102 |
| abstract_inverted_index.Chinese-English | 98 |
| abstract_inverted_index.multi-attention | 71 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5060632261 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I167383011 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.71604841 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |