Note: This blog post was originally written in Japanese for our Japanese website. We use our machine translation platforms to translate and make automatic corrections, and then partially edit to fit the content in English.
The quality of AI translation, including machine translation (MT) and large language models (LLMs), has improved dramatically in recent years, and many companies now use it for multilingual expansion, customer support, and technical document management. But no matter how far the technology advances, not all language pairs translate equally well. MT handles some pairs especially well, while others are naturally harder.
If you want to translate efficiently with MT or LLMs, this post explains which language pairs are best suited to them, meaning those where high-quality output is more likely, along with the linguistic characteristics and data factors behind that.
MT quality depends not only on engine performance but on a combination of the following factors.
Languages can be classified by many characteristics, such as word order, grammatical structure, inflection, and lexical structure. Language pairs that share these characteristics are easier for MT to handle and result in fewer translation errors. English and Spanish, for example, both follow SVO (subject-verb-object) word order and have similar lexical structures, which makes it easier for models to learn semantic correspondences between them.
MT is built on large amounts of parallel data. The more widely a language is used internationally, the more translation data exists and the more accurately a model can learn. Languages with small user bases, or language pairs with limited parallel corpora, face a shortage of training data that makes high performance difficult to achieve.
Highly context-dependent languages such as Japanese and Arabic, and languages where word forms change significantly with context, such as those with honorifics, pose greater challenges for MT.
So which language pairs does MT handle best?
This combination tends to deliver the most stable MT accuracy, for three reasons:
Word order variations and noun gender add some complexity, but research in this area has been ongoing for many years and training data is plentiful. Since the advent of neural machine translation (NMT), translation quality for these languages has improved significantly.
Although they’re not as widely spoken as the Romance languages, their vocabulary and grammar are similar to English, which makes them well suited to MT. Their simple grammatical structure also makes it relatively easy to achieve high-quality results in business documents.
A term that comes up often in MT is “typologically distant.” It describes language pairs whose structures differ significantly, making it hard for a model to learn semantic correspondences. The following pairs are considered especially challenging for MT.
Key structural differences include:
Because of these differences, MT systems must restructure entire sentences, which makes errors more likely.
Key structural differences include:
Because of these differences, MT has to bridge a wide structural gap, which makes errors more likely.
Translation with LLMs has become widespread in recent years, and these models are expected to understand context and interpret intent beyond the capabilities of traditional MT. Even so, the challenges of typologically distant language pairs don’t disappear entirely. For example:
Stable MT performance depends on the combination of model strength, language pair compatibility, and a design tailored to the intended use.
When using MT, it’s easy to get caught up asking which engine is the best. But the real key to success is understanding what level of quality you can expect for each language pair. Keep the following in mind:
Factor these in, and you can build realistic and effective plans, from budgets and schedules to a post-editing framework.
We hope this post serves as a helpful reference for your MT projects.
Once you know what quality to expect for your language pair, the next step is choosing a tool and workflow that fit your project.
With XMAT, our AI translation platform, you get high-quality, secure machine translation for both text and documents. You can also use the translation editor and the AI-powered (LLM) revision feature to refine your results.
At Kawamura International, we do more than provide machine translation. We offer a range of solutions to help streamline your translation processes, including post-editing by experienced professionals. If you’re planning an MT project and want to know what quality to expect for your language pair, we’d love to help. Let’s talk.