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...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 12; p. 7126
Main Authors Ahmed, Maimoonah, Ouda, Abdelkader, Abusharkh, Mohamed, Kohli, Sandeep, Rai, Khushwant
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Ambiguity
Automation
Computational linguistics
Computer software industry
corpus-based translation
Correspondence
cross-lingual information retrieval
English language
Intellectual property
Language
Language processing
Linguistics
Machine learning
Machine translation
Morphology
Natural language
Natural language interfaces
natural language processing
Probability
Syntax
technical patents
Translating and interpreting
Translation
translation quality
Translations
United States
United Kingdom
Japan
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13127126