Forecast for Neural Machine Translation
Neural Machine Translation (NMT) has made significant strides in recent years, impacting the breaking down of language barriers. As a fast explanation - neural machine translation is an approach that uses an artificial neural network instead of statistical patterns.?
Initially, for the masses, it was announced in September 2016 by Google and is continuously improving. For example, in 2016, the translation accuracy for the English-Spanish pair was 80%, while now it is 97%. Such a successful development of technology provoked a high interest in it, so we wanted to explore what could happen next with Neural MT and share the information with you.
The Neural Machine Translation Statistic
According to Fact. MR research, the market value of machine translation is predicted to grow significantly from $60 billion to $96 billion in the next eight years. The development of translating technologies also impacts the variety of languages on the web - over 50% of all Google queries are now made in languages other than English, underlining the diverse linguistic needs of today's users.?
The impact of NMT is felt across multiple sectors, such as business, healthcare, e-learning, e-commerce, etc. Its swift and accurate translations have opened doors for global collaboration and communication. NMT has excellent context understanding, and its improvement raises the quality of the result, which can, in the future, significantly change the entire industry.??
For now, there is a list of well-known NMT providers like Google, Amazon, Microsoft Translate, DeepL, etc. Each of them has its advantages and drawbacks. For example, Google supports 133 languages, while DeepL only supports 28. On the other hand, DeepL has better accuracy for common language pairs like - English --> German/French/Spanish and vice versa (find the statistic below.) That is why, at Lingohub, we use a combination of translation engines to provide the most accurate result for our Machine translation suggestions.?
The Neural Machine Translation Trends
Machine translation post-editing (MPTE)
The time it takes to release a product determines its success in the market. Even if you have a new feature before your competitors, it will only matter if it's localized, especially in sensitive industries like Fintech. Without language versions, you can't provide the value to your user. MPTE is an approach that speeds up the time to market by using neural machine translation because your linguists aren't working with empty fields from the start but editing the automated pre-translation.?
Adaptation instead of literal translation
Context understanding allows modern translation systems to provide non-literal translation where appropriate. For example, direct questions can come across as impolite in English, and the imperative form of the verb can sound rough.?
The same situation is with idioms or regular expressions. For instance, while "on the one hand/on the other hand" is a common phrase in English, in Ukrainian, it may be more appropriate to use "On the one side/on the other side."
Multilingual SEO
Prioritizing multilingual SEO is imperative for achieving success in the global market. Research shows that users overwhelmingly prefer resources in their native language, and a staggering 40% won't even consider purchasing from a website that isn't in their language. While English continues to be popular, there are billions of native speakers of other languages. A comprehensive multilingual SEO strategy that includes optimizing site content, updating metadata such as title and description, building links, and more is essential for achieving the desired results.?
The Bottom Line
Neural machine translation has a high potential, and its future consists of constant improvement, neural network teaching, and strengthening of the engines.?
Nowadays, we can't say that NMT can fully handle the translation challenges. Neural networks can be a great support tool but not replace human translators. Will this ever change? Quite possibly, but not in the near future.