Why is Machine Translation into Japanese Difficult?

Machine translation has come a long way in recent years, with advancements in artificial intelligence and natural language processing making it possible to translate text from one language to another with remarkable accuracy.

However, when it comes to Japanese, the different syntax and grammatical constructs of the language pose a unique challenge for machine translation, especially when compared to Romance languages like French and Spanish.

Challenges of Translating from English to Japanese

One of the main difficulties with translating from English to Japanese is the subject-object-verb (SOV) sentence structure that is used in Japanese, as opposed to the subject-verb-object (SVO) structure used in English. In Japanese, the subject typically comes at the beginning of the sentence, followed by the object and then the verb. This can make it challenging for machine translation systems to accurately translate English sentences, as they need to rearrange the words to conform to the Japanese sentence structure.

For example, consider the following sentence in English:

“I eat sushi.” In transliterated Japanese, this would be translated as “Watashi wa sushi wo tabemasu.” The subject “watashi” (meaning “I”) comes first, followed by the object “sushi,” and finally the verb “tabemasu” (meaning “eat”).

This means that the machine translation system needs to first identify the subject, object, and verb in the English sentence, and then rearrange them to fit the Japanese sentence structure.

Another challenge with translating from English to Japanese is the use of particles. In Japanese, particles are small words that are attached to nouns or pronouns to indicate their grammatical function in the sentence. For example, the particle “wa” is used to mark the topic of a sentence, while the particle “wo” is used to mark the direct object. This means that the same word in English can be translated differently depending on its function in the sentence.

For example, consider the sentence “I eat sushi with chopsticks.” In transliterated Japanese, this would be translated as “Watashi wa hashi de sushi wo tabemasu.” Here, the particle “wa” marks “watashi” as the topic of the sentence, while the particle “de” indicates that “hashi” (meaning “chopsticks”) is the means by which the action is performed. Again, the machine translation system needs to accurately identify the function of each word in the English sentence and then choose the appropriate particle in Japanese.

Compared to Japanese, Romance languages like French and Spanish have a much more similar sentence structure to English, with an SVO structure being used in both languages. This means that machine translation systems can more easily translate between these languages, as the basic word order is the same. Additionally, while these languages have their own unique grammatical constructs, they tend to be more similar to English than Japanese, making it easier for machine translation systems to identify and translate them.

Neural Machine Translation

Neural machine translation (NMT) is a type of machine translation that uses neural networks to learn how to translate between languages. Compared to traditional rule-based or statistical machine translation systems, NMT has shown significant improvements in translation quality and is able to handle some of the syntactic and grammatical challenges of Japanese.

One advantage of NMT is its ability to learn and generalize patterns in language, including the word order and grammar of different languages. NMT models are trained on large amounts of bilingual text, which allows them to learn the correspondence between the source language (in this case, English) and the target language (Japanese). This means that NMT models can potentially learn to rearrange English sentences into a more Japanese-like order, and to correctly use particles in Japanese sentences.

Moreover, recent advances in NMT have also addressed some of the specific challenges of translating between English and Japanese. For example, researchers have developed methods to handle the differences in word order between the two languages, such as using position embeddings to encode the position of words in a sentence.

NMT can make errors, particularly in translating complex or idiomatic expressions. Additionally, NMT models require large amounts of training data and computational resources, which can be a limitation in low-resource settings.

While neural machine translation has the potential to address some of the challenges of Japanese translation, it is not a panacea and still requires additional human oversight and intervention to ensure accurate and high-quality translations. More time is required to post-edit machine-translated Japanese text than translations into European languages. Unique processes must be applied to Japanese to reach the standard of quality required by ISO 18587.

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