Machine Translation provides many benefits, from ensuring translation quality and consistency to eliminating time-consuming manual processes. When used effectively, it provides significant returns to businesses and allows local brands to transform into global companies.
Many businesses are turning to machine translation (MT) tools to accelerate market penetration and expand into new regions. While the concept of machine-assisted translation has been around for many years, machine learning technology has only recently become applicable and widely accessible. Today, advances in artificial intelligence and natural language processing release the true potential of these tools and expanding their applications.
How does machine translation work?
Although there are various types of machine translation options, most of them follow the same basic principle. Machine translation works based on learning models and advanced algorithms to translate text or speech input from the source language to the target language.
In the first stage of the process, the source text or speech is filtered and edited. The machine translation is then trained with extensive examples from both the source and target languages, which provides them with the information they need to pick and adopt to patterns and possibilities in text and speech, enabling systems to better understand which words to choose and how expressions are structured.
Machine translation applications can then produce reliable text translation based on information learned from training. While advanced neural networks powered by AI provide incredibly accurate results, some improvements from human translators are often needed. However, the need for modification and post-editing is much less than in the past when rule-based systems were the norm.
The 3 most commonly used types of machine translation
There is no one-size-fits-all approach to machine translation. Today, there are several options available if you want to reconvert content from one language to another.
The route to choose depends on your needs. Below are some of the most common solutions you will encounter.
Rules-based machine translation (RBMT)
As the name suggests, rule-based machine translation relies on strict rules and programmed dictionaries to provide results. The system takes predetermined information into account in the original text to decide the best word or phrase in the target language. An RBMT application takes into account sentence structure, language rules, and meaning to make this decision.
Although rules-based machine translation engines can be effective, there are some limitations. First, while a complete dictionary is needed, comprehensive language rules must also be available, which must reveal detailed information about the sentence structure in both the source and target languages. Another major drawback of such translation tools is that they require constant manual input to account for language changes. The language we use evolves over time, so outdated rules can affect the reliability and relevance of translations.
Statistical machine translation (SMT)
Statistical machine translation is a more advanced alternative to RBMT systems that use previously translated text as a reference point. SMT systems use these human-generated translations to evaluate entire sentences rather than individual words. As a result, more natural and accurate translations are delivered.
But there is more to statistical machine translation than this. In addition to analyzing human-translated sentences, SMT systems comprehensively analyze source and target languages using computational linguistics to model and produce relatively reliable results. It was SMT that pioneered some of the earliest and most widely used machine translation engines, such as Google Translate.
On the other hand, this type of machine translation engine also has its limitations. SMT systems need to be trained to provide accurate results and can only provide translation if there is a reference point.
That's why machine translation providers like Google Translate, Amazon Translate, and Microsoft Translator have all embraced the deep learning potential of neural networks.
Neural machine translation (NMT)
Today, Neural machine translation is considered best in class. It goes beyond the limits of statistical systems and rule-based machine translation engines, becoming more refined and reliable with every translation task it completes. Neural machine translation systems are much more adept at identifying language patterns in the original text and understanding context.
Neural machine translation, often called deep learning, is based on artificial intelligence and does not require any training from human linguists. Neural machine translation systems create an ever-expanding network of information that closely models the human brain, can process large data sets to determine the best possible translations. Thanks to the consistency and efficiency of NMT systems, they can significantly speed up and streamline the translation workflow.
Origin of machine translation
Many people consider machine translation to be a fairly modern invention. However, machine translation technology has been in use for decades. The concept of machine translation emerged in the 1940s, when scientists were tasked with finding a reliable method for translating scientific documents and military communications. In 1954, the Georgetown-IBM Experiment revealed the potential of machine translation with a simple, rule-based system that translated dozens of sentences from Russian to English.
Rule-based machine translation has evolved over the last few decades. However, by the 1980s, even the most advanced systems struggled to cope with the complexity of language rules, while the demand for extensive manual input from human linguists made most translation engines impractical.
By the 1990s, rule-based machine translation was replaced by statistical machine translation. As the online age finally in full swing, vast amounts of linguistic data have become accessible to provide statistical models with the information they need to examine words and sentence structure and evaluate probabilities. Syntax-based systems were also pioneered during this time, and this approach was designed to compensate for the shortcomings of statistical-based systems.
In the 2010s, the dawn of neural machine translation had finally arrived. Now that technology has made it possible to create virtual neural networks, the translation process can be largely automated without the need for extensive training and predetermined language rules. Although the first versions of Google Translate and similar translation engines were not particularly reliable, advances in neural machine technology have begun to provide increasingly reliable results as standard.
Where is machine translation used?
Machine translation software has been widely adopted in almost every industry sector for both personal use and business applications. Below are just a few of the ways machine translation is being put to use today.
An effective tool for data analysis
The most advanced machine translation tools can process vast amounts of data and deliver reliable results in a very short time. International companies often use machine translation to translate content from social media channels and websites, using the results for analytical purposes.
These results can be used to gather information from customer reviews or social media posts written in many different languages, making it easier for brands to reposition marketing messages across various markets.
For internal communication in international organizations
Businesses face many obstacles in inhouse communication One of these is internal communication. As brands expand and open locations in new regions, staying connected is vital to success, but there's no guarantee that everyone will be fluent in the same language.
Machine translation can help reduce problems caused by language barriers. Whether it's an email from a CEO or a corporate newsletter, machine translation can be used to capture the core meaning of the original message.
Provides reliable results for external communications
Machine translation is not only used for internal company communication. With the advent of reliable neural machine translation, it has become possible to communicate safely with customers and stakeholders in other languages.
Important business documents can be translated from one source language into many different languages, allowing brands to reach a new target market or connect with international partners. Machine translation apps can also be used to translate content such as customer reviews, giving non-native speakers the chance to access the other perspectives before making a purchase.
Target customers in any language
Customer service can be significantly improved using machine translation. Machine translations make it possible for every customer to get the right note, no matter where they are located.
A large number of customer requests in many different languages can be easily met. Meanwhile, machine translation can significantly improve live chat functionality, eliminating the need for human support representatives.
What are the benefits of machine translation?
The growing potential of neural networks means there's never been a better time to adopt machine translation technology. Companies are increasingly relying on machine translation to support workflows and deliver results faster and more efficiently. Still not convinced? Below are just a few of the benefits of machine translation.
Automation
Even if you rely on human translation teams, machine translation is also an effective tool. Translation management systems often use multiple translation applications to support workflows. This hybrid machine translation approach means automating much of the hard work, taking the pressure off human linguists and giving them more time to focus on post-editing.
Predictive analytics
Predictive analysis is an important part of today's machine translation engines. Companies that rely on statistical models to capture key trends and patterns in raw data can use predictive analytics to gain insight into customer preferences and consumer behavior, which can help guide decision-making about the products they sell and the services they offer.
Continuous learning
One of the most exciting features about neural machine translation is that it continuously learns, using an ever-updating dataset to provide up-to-date and consistently relevant results, which eliminates many shortcomings of older machine translation models such as rule-based systems.
Cost Saving
Whether used independently or to support human linguists, machine translation tools can provide significant cost-saving benefits to organizations. Even technology, when used with teams of human translatorsspeeds up workflows, allowing companies to deliver projects faster.
Image and video analysis
Optical character recognition (OCR) technology is another element of machine translation that makes it possible to analyze image and video content in other languages. Image translation is now widely accessible, as services such as Google Translate allow ordinary users to translate content via their smartphone's camera. Even if the technology is still quite limited, it can be a useful tool for businesses looking to expand their reach into new regions.
Customer information
If you want to improve customer experience, you first need to access customer insights. If you are exploring new markets residing non-native English speakers, the process can be time-consuming and costly. Advanced machine translation tools allow you to accurately examine consumer sentiment, making it easier to reposition multilingual marketing messages for new territories.
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