An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models
This paper addresses the challenges associated with machine translation of patents from English to Japanese. This translation poses unique difficulties due to their legal nature, distinguishing them from general Japanese-to-English translation. Furthermore, the complexities inherent in the Japanese...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/12/7126 |
| _version_ | 1827738736231710720 |
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| author | Maimoonah Ahmed Abdelkader Ouda Mohamed Abusharkh Sandeep Kohli Khushwant Rai |
| author_facet | Maimoonah Ahmed Abdelkader Ouda Mohamed Abusharkh Sandeep Kohli Khushwant Rai |
| author_sort | Maimoonah Ahmed |
| collection | DOAJ |
| description | This paper addresses the challenges associated with machine translation of patents from English to Japanese. This translation poses unique difficulties due to their legal nature, distinguishing them from general Japanese-to-English translation. Furthermore, the complexities inherent in the Japanese language add an additional layer of intricacy to the development of effective translation models within this specific domain. Our approach encompasses a range of essential steps, including preprocessing, data preparation, expert feedback acquisition, and linguistic analysis. These steps collectively contribute to the enhancement of machine learning model performance. The experimental results, presented in this study, evaluate three prominent alternatives considered for the final step of the transformer model. Through our methodology, which incorporates a modified version of NLP-Model-III, we achieved outstanding performance for the given problem, attaining an impressive BLEU score of 46.8. Furthermore, significant improvements of up to three points on the BLEU score were observed through hyperparameter fine-tuning. This research also involved the development of a novel dataset consisting of meticulously collected patent document data. The findings of this study provide valuable insights and contribute to the advancement of Japanese patent translation methodologies. |
| first_indexed | 2024-03-11T02:48:32Z |
| format | Article |
| id | doaj.art-32f8867c70c24dbba7a4dedc9f40dd23 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T02:48:32Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-32f8867c70c24dbba7a4dedc9f40dd232023-11-18T09:09:22ZengMDPI AGApplied Sciences2076-34172023-06-011312712610.3390/app13127126An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation ModelsMaimoonah Ahmed0Abdelkader Ouda1Mohamed Abusharkh2Sandeep Kohli3Khushwant Rai4Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, CanadaDepartment of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, CanadaDigital Media Software Engineering, Ferris State University, Grand Rapids, MI 49307, USAVillage Centre Pl Unit 209, Mississauga, ON L4Z 1V9, CanadaVillage Centre Pl Unit 209, Mississauga, ON L4Z 1V9, CanadaThis paper addresses the challenges associated with machine translation of patents from English to Japanese. This translation poses unique difficulties due to their legal nature, distinguishing them from general Japanese-to-English translation. Furthermore, the complexities inherent in the Japanese language add an additional layer of intricacy to the development of effective translation models within this specific domain. Our approach encompasses a range of essential steps, including preprocessing, data preparation, expert feedback acquisition, and linguistic analysis. These steps collectively contribute to the enhancement of machine learning model performance. The experimental results, presented in this study, evaluate three prominent alternatives considered for the final step of the transformer model. Through our methodology, which incorporates a modified version of NLP-Model-III, we achieved outstanding performance for the given problem, attaining an impressive BLEU score of 46.8. Furthermore, significant improvements of up to three points on the BLEU score were observed through hyperparameter fine-tuning. This research also involved the development of a novel dataset consisting of meticulously collected patent document data. The findings of this study provide valuable insights and contribute to the advancement of Japanese patent translation methodologies.https://www.mdpi.com/2076-3417/13/12/7126machine translationtechnical patentsnatural language processingtranslation qualitycross-lingual information retrievalcorpus-based translation |
| spellingShingle | Maimoonah Ahmed Abdelkader Ouda Mohamed Abusharkh Sandeep Kohli Khushwant Rai An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models Applied Sciences machine translation technical patents natural language processing translation quality cross-lingual information retrieval corpus-based translation |
| title | An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models |
| title_full | An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models |
| title_fullStr | An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models |
| title_full_unstemmed | An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models |
| title_short | An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models |
| title_sort | optimized approach to translate technical patents from english to japanese using machine translation models |
| topic | machine translation technical patents natural language processing translation quality cross-lingual information retrieval corpus-based translation |
| url | https://www.mdpi.com/2076-3417/13/12/7126 |
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