Ahmed, M.; Ouda, A.; Abusharkh, M.; Kohli, S.; Rai, K. An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models. Appl. Sci.2023, 13, 7126.
Ahmed, M.; Ouda, A.; Abusharkh, M.; Kohli, S.; Rai, K. An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models. Appl. Sci. 2023, 13, 7126.
Ahmed, M.; Ouda, A.; Abusharkh, M.; Kohli, S.; Rai, K. An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models. Appl. Sci.2023, 13, 7126.
Ahmed, M.; Ouda, A.; Abusharkh, M.; Kohli, S.; Rai, K. An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models. Appl. Sci. 2023, 13, 7126.
Abstract
Over the years, machine learning has emerged as a tool for automated translation and has been studied relentlessly for decades. RBMT, SMT, and NMT models have been used to achieve machine translation and the results have drastically improved from when research in this field first began. Although a few general-purpose translators such as Google Translate or Microsoft Translator have accurate translations compared to that of a human translator, many pieces of text containing highly technical terms or homonyms are often mistranslated completely. When considering the necessity and importance of translating technical patents from different domains, accuracy in translation is not something that can be compromised. This motivates the need to improve the performance of machine translation further. The scope of this paper covers three open-source machine translation models for the purpose of patent documentation translation from English to Japanese, evaluates their performance on patent data, and proposes a methodology that enabled us to improve one of the model’s BLEU score by 41.22%, achieving a BLEU score of 46.18.
Keywords
Machine Translation; Technical Patents; Natural Language Processing; Translation Quality; Cross-Lingual Information Retrieval; Corpus-based Translation; Domain Adaptation; Language Model Fine-tuning; Neural Machine Translation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
