The Future of Machine Translation
In this era of globalization, the demand for language translation has increased manifold. This is because many businesses want to expand their operations in foreign countries. Large data is the buzzword in the business world. Technology innovation has impacted every field of the world. The translation industry is not an exception. Machine translation services are helping thousands of companies to operate internationally. Let’s have a look at what is machine translation.
What is Machine Translation (MT)?
Machine Translation is the automated translation process in which content is translated from one source material to another language. It is a new technology for the general public. However, it is around the market for decades.
Do you know that SYSTRAN is among the first companies that developed Machine translation systems in the late 1960s? This company cooperated with the U.S. Air Force which used to translate intelligence material during the Cold war. The objective of MT is to provide translated content to the human translator so that they can remove the errors. Usually, the machine translation engines worked on ruled-based methods. These methods include rules developed by humans and dictionaries for a smooth transition of languages. That time MT has greatly evolved with time. Today we have machine translation software that is way ahead of those early systems, utilizing advanced techniques like statistical machine translation (SMT) and neural machine translation (NMT). These modern methods rely on large databases of existing translations and sophisticated algorithms to learn linguistic patterns, rather than just following pre-set rules. This shift has allowed for more natural and accurate translations, capturing nuances and context better than before.
Factors Influencing the Machine Translation
Let’s have a look at factors that are influencing the machine translations evolution
Statistical Models
The major development in machine translation started in the 1990s, This was the time when IBM started to take leverage from statistical models to improve translation quality. The statistical model is a unique technology. They use advanced statistical models and a large amount of data from the internet to translate the content. Google will later or sooner use this technology to make all human knowledge searchable.
Statistical machine translation engines are better than rule-based engines but they can also make errors. Therefore, to tackle this challenge, statistical MT with a rule-based MT used. The amalgamation of two translation models known as hybrid MT engines. Many companies adopted this model on a global scale.
Neural Machine Translation
The new technological advancement in machine translation is witnessed in 2017 with the advent of Neural machine translation. Neural MT services equipped with the power of Artificial Intelligence AI and use neural networks to generate to provide translations.
It imitates the thought process of the translator instead of guessing the outcome. Therefore, the translation result is natural because it takes the intricacies of the sentence into view. This development resulted in MT that can translate a large number of documents that are used for both technical and non-technical purposes.
Do you know that Neural MT has addressed many shortcomings of MT by improving the readability of automated translations? In addition, it’s incompatible with other languages like Korean. Improvements and innovations in MT are still in process.
Which Type of Machine Translation Should You Use?
Generally, Statistical MT is common because it does not require a constant updation of language rules like Rule-based MT. On the other hand, Neural MT is the most advanced option. Deciding which type of machine translation should you use depends upon many factors.
- Budget: As compared to other types of MT, neural MT is the most expensive engine to use. However, its high price is worth it according to its translation standards.
- Industries: If you are dealing with complex technical content then neural machines can provide technical translation services.
- Language Pairs: Statistical MT is a viable option if you want to translate Latin-based language pairs because they have similar grammatical rules and syntax.
- Different Purposes: For internal business communication, you can use simple MT to save money. On the contrary, for sales and marketing material, the intervention of human translators is important because they can translate the content while taking regional and cultural nuances into view. MT can be used for translation whereas MTPE is used for post-editing.
Driving Factors for Adopting Neural Machine Translation
Neural MT is expected to be adopted at a faster pace in the future because the requirement for localization has increased exponentially. Covid-19 has made many companies transform their operations digitally. This has enhanced the need for targeted and diversified content. Therefore, all these changes in the market have increased the demand for MT.
Human translation is made accountable with MTPE services which is the amalgamation of both machine and traditional human translation. Post-editing improves the quality of the translated text. Companies are expecting translation services to become more affordable for a few languages. The cost reduction enables companies to tap into different markets without any communication barriers.
Wrapping Up
The adoption of NMT is a step forward toward digital transformation in the global economy. This competitive landscape is making companies opt forMT. If you are running a technical company then you must select a neural machine to provide customers with impeccable professional translation services in their native language. In the future, you will see that use of MT will become a norm. Which machine translation model do you like the most?
