###### Abstract  
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. 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 +
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encode that meaning back into the target language. However, when we break down these two steps, the hidden complexities beneath the surface become clearer. To achieve an accurate translation, the translator must have a comprehensive understanding of the components representing languages, such as syntax, semantics, lexicology, etc., of both the source and target languages as well as the culture of the speakers of both languages. Or at least, that is what traditional, also referred to as rule-based translation, aims at. Additionally, lexical ambiguity further confounds the translation process. Often, words that are either homonyms or polysemous cause lexical ambiguity because they are words that have various meanings, and a translator must identify the expected meaning from context to choose the right word in the target language [1]. Inflectional morphemes are also a source of lexical ambiguity. For example, the word number in the English language may be a noun, or it may be the inflected form of numb [1]. This makes selecting the correct translated option meticulous work. On the other hand, words that are not ambiguous in the source language may be open to more than one interpretation in its target language. This lack of one-to-one correspondence between words in different languages makes translation and inherently automating it a complex task. A different origin of complexity in translation is syntactic ambiguity where the syntax of a sentence leads to more than one meaning. This type of ambiguity is particularly more complex to a computer because humans are able to pick up on the intended meaning through context, while computers have a difficult time discerning multiple possible meanings [1]. For instance, it is clear to humans that the sentence, \"The stolen wallet was found by the fire hydrant\" means that the wallet was found next to the fire hydrant; however, a computer might interpret it as the fire hydrant finding the stolen wallet. Evidently, these ambiguities, among many others, make it very difficult for a computer to represent the structure of a language in the form of rules. Translation becomes a more complex task to do when patent documentation is introduced into the mix, which involves patent conditions, correspondence with lawyers, and a unique style of writing [4].