## 3 English-to-Japanese Patent Machine Translation  
The translation problem we are tackling focuses on translation from English to Japanese, specifically on translating technical patents in different domains from English to Japanese. The purpose of this is to understand the complexities of language translation for relevant use cases such as patent translation. The vast dissimilarity (lexically, syntactically, morphologically, and semantically) between the two languages will aid in highlighting some of these complex problems that are encountered in MT with the purpose of studying to mitigate them. One source of distinctions other than the obvious difference in characters of both languages is the word order in English and Japanese. In English, the word order is in the form of subject-verb-object (SVO), while the word order in Japanese is SOV [17]. This means that topics are at times expressed in entirely different sentence structures, and thus there is a lack of correspondence between the structures of sentences, which poses a challenge for the machine to translate accurately [18]. Additionally, being a high-context language, it is common in Japanese for much of the information to be implied [19]. For example, the subject is usually dropped in situations where the context is clear [20]. If a person were to say \"I am going to bake a cake\" in English, the direct Japanese translation would be \"bake a cake\" since it is understood by the listener that the speaker is the subject, and thus \"I\" is implied. In contrast, English, as a low-context language, communicates content explicitly, and writing is understood very literally. This demonstrates that there is more to translation than ensuring the machine translator chooses the correct words; the cultural context must also be taken into account to make sure that the intended meaning isdelivered correctly [21]. This could prove an unfeasible task for computers; being a cultural mediator requires a certain level of refined reasoning where one must be able to deduce what meanings might be extracted by a reader so that the translation may be adjusted as needed [21]. Without the explicit specification of a large number of language and cultural context-based rules, this task is difficult for machines, and if such intricate rules, which may be in the tens of thousands, were to be constructed and documented with the system, the effort expended may be prohibitive and contradictory to the purpose of utilizing machine learning to automate the MT task altogether. A system that overcomes the need to explicitly set language-dependent or culture-dependent rules is the ultimate goal here. MT using machine learning is a candidate to solve this problem. The problem of context sensitivity is more or less avoided when considering the source and target text that is shared by readers with the same background knowledge [21]; for example, readers may be part of the same scientific discipline or industry. The premise of this work will focus on the translation of technical patents from English to Japanese using machine translation. Although the complexity of context sensitivity is reduced, we are faced with another challenge: patents of scientific nature contain many technical words that are domain-dependent and may be homonyms with more than one interpretation depending on the subject. Instead of running into the problem of deciphering cultural context, the machine must determine the correct translation of a word depending on the domain it is used in. For example, the word \"arm\" is a homonym for both the biological human arm as well as a technological robotic arm in English. In Japanese, it becomes sensitive to the domain it is used in; \"\(\backslash\)\" refers to a human arm, while \"\(\mathcal{T}\)\" \(\mathcal{L}\)\" refers to a mechanical arm. The world has grown increasingly connected and more technologically advanced, and this is reflected by the increase in patent applications in new and different technology spaces over the years.