Many businesses are turning to machine translation (MT) tools to speed up time to market and expand into new territories. While the concept of machine-assisted translations has been around for many years, it’s only fairly recently that machine-learning technology has become viable and widely accessible. Today, advancements in artificial intelligence and natural language processing are unlocking the true potential of these tools and broadening their applications.
Machine Translation brings many exciting benefits, from guaranteeing translation quality and consistency to eliminating time-consuming manual processes. When used effectively, they yield significant returns for businesses, allowing homegrown brands to become global enterprises. However, not every machine translation solution is created equal. Below, we’ll explore how machine translation actually works, the history of the technology, and how you can deploy it effectively.
How machine translation works
Although there are several types of machine translation options out there, most of them follow the same basic principle. Machine translation works by relying on learning models and sophisticated algorithms to translate text or speech input from a source language into a target one.
During the first stage of the process, source text or speech is filtered and organized. Next, the machine translation is trained with extensive examples from both the source and target language. This provides machine translation systems with the information they need to comprehend patterns and probabilities in text and speech. This allows these systems to better understand which words to select and how phrases are structured.
Machine translation engines are then able to produce reliable text and speech translation based on the insights learned from training. While advanced neural networks powered by AI provide incredibly accurate results, there’s often a need for some refinement by human translators. However, the need for adjustment and post-editing is far less than it once was when rule-based systems were the standard.
3 most common types of machine translation
There’s no one-size-fits-all approach to machine translation. Today, there are several options available if you’re looking to repurpose content from one language into another. Which route you take depends on your requirements. Below are some of the most common solutions you’ll encounter.
Rule-based machine translation (RBMT)
As the name suggests, rule-based machine translation relies on strict rules and programmed dictionaries to deliver results. This predetermined information considers the original text to decide the best word or phrase from the target language. An RBMT engine looks at sentence structure, language rules, and meaning to make this decision.
While rule-based machine translation engines can be effective, there are some limitations. For one, a complete dictionary is required, while extensive language rules also need to be in place. These rules need to lay out detailed information regarding sentence structure in both the source and target language. Another major drawback of this type of translation tool is that it requires constant manual input to account for language changes. The language we use evolves over time, so antiquated rules can impact the reliability and relevance of translations.
Statistical machine translation (SMT)
Statistical machine translation is a more advanced alternative to RBMT systems, using pre-translated text as a reference point. SMT systems use these human-generated translations to assess entire phrases, rather than individual words. The result is more natural-sounding and accurate translations.
However, there is more to statistical machine translation than this. Along with analyzing phrases translated by humans, SMT systems extensively analyze the source and target languages, using computational linguistics to model and generate relatively reliable results. It was SMT that spearheaded some of the earliest and widely available machine translation engines like Google Translate. That being said, this type of machine translation engine has its limitations. SMT systems need to be trained in order to deliver accurate results and can only provide translations if it has a point of reference. Because of this, machine translation providers like Google Translate, Amazon Translate, and Microsoft Translator have all embraced the deep learning potential of the neural network.
Neural machine translation (NMT)
Today, neural machine translation is considered the best-in-class solution. It moves 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 engines are far more adept at identifying linguistic patterns in original text and understanding context.
Often referred to as deep learning, neural machine translation relies on artificial intelligence and requires no training from human linguists. By creating a constantly expanding neural network closely modeled on the human brain, neural machine translation engines can handle vast datasets to determine the best possible translations. Thanks to the consistency and efficiency of NMT systems, they can dramatically speed up and streamline translation workflow.
The origin of machine translation
Many people consider machine translation a fairly modern invention. However, machine translation technology has been around for decades. The concept of machine translation emerged in the 1940s, with scientists tasked with finding a reliable method for the translation of scientific documents and military communications. In 1954, the Georgetown-IBM Experiment revealed the potential of machine translation, with a rudimentary rule-based system translating dozens of sentences from Russian into English.
Rule-based machine translation advanced over the next few decades. However, until the 1980s, even the most advanced systems struggled to cope with the intricacies of linguistic rules, while the demand for extensive manual input from human linguists rendered most translation engines impractical.
By the 1990s, rule-based machine translation had been superseded by statistical machine translation. With the online age finally in full swing, vast sets of language data could be accessed to give statistical models the information they needed to study words and sentence structure, as well as assess probability. Syntax-based systems were also pioneered during this time, with this approach designed to compensate for the shortcomings of statistical-based ones.
In the 2010s, the dawn of neural machine translation had finally arrived. Now that technology made it possible to create virtual neural networks, the translation process could be largely automated, without the need for extensive training and predetermined linguistic rules. While early versions of Google Translate and similar translation engines weren’t particularly reliable, advancements in neural machine technology have delivered increasingly reliable results as standard.
How is machine translation used?
Machine translation software has been widely adopted, both for personal use and for business applications across just about every industry sector. Below are just a few of the ways machine translation is put to use today.
An effective tool for data analysis
The most advanced machine translation tools can process huge volumes of data, delivering reliable results in no time at all. 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 insights from customer reviews or social media points written in many different languages, making it easier for brands to reposition their marketing messages in various markets.
For internal communications in international organizations
Internal businesses face a lot of obstacles. One of these is internal communications. As brands expand and establish branches in new territories, staying connected is vital to success. However, there’s no guarantee that everyone is going to be speaking the same language fluently.
Machine translation can help mitigate the problems caused by the language barrier. 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.
Delivers reliable results for external communications
Machine translation isn’t just used for internal communication. With the advent of reliable neural machine translation, it’s possible to communicate with customers and stakeholders in other languages with confidence.
Key business documents can be retooled from a source language into many different ones, allowing brands to reach out to a new target market or connect with international partners. Machine translation engines can also be used to translate content like customer reviews, giving non-native speakers the chance to see what others are saying before they commit to a purchase.
Target customers in any language
Customer service can be dramatically enhanced by using machine translation. No matter where they’re based, machine translations make it possible to strike the right note with every customer.
Huge volumes of customer requests in many different languages can be comfortably handled. Meanwhile, machine translation can dramatically improve live chat functionality, negating the need for human support agents.
What are the benefits of machine translation?
The increasing potential of neural networks means there’s never been a better time to embrace machine translation technology. Companies are increasingly depending on machine translation to support workflows and deliver results more quickly and efficiently. Still not convinced? Below are just a few of the benefits of machine translation.
Even if you’re relying on human translation teams, machine translation is an effective tool. Translation management systems typically rely on multiple translation engines to support workflows. This hybrid machine translation approach means that much of the hard work is automated, freeing up pressures on human linguists and providing them with more time to focus on post-editing.
Predictive analytics is a key part of modern-day machine translation engines. Relying on statistical models to capture key trends and patterns in raw data, companies can use predictive analytics to gain insights into customer preferences and consumer behavior. This can help steer decision-making about the products they sell and the services they offer.
One of the most exciting things about neural machine translation is that it continuously learns, using a constantly updated dataset to provide results that are up-to-date and consistently relevant. This avoids many of the shortcomings of older machine translation models, such as rule-based systems.
Whether machine translation tools are being used independently or to support human linguists, they can deliver significant cost-saving benefits to organizations. Even when used alongside teams of human translators, the technology can expedite workflows, allowing companies to deliver projects more quickly.
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, with services like Google Translate allowing everyday users to translate content through the camera of their smartphone. While the technology is still fairly limited, it can be a useful tool for businesses looking to extend their reach into new territories.
If you want to improve customer experience, you first need access to customer insights. If you’re exploring new markets with non-native speakers, this process can be time-consuming and costly. Sophisticated machine translation tools allow you to accurately study consumer sentiment, making it easy to reposition multilingual marketing messaging for new territories.
How BLEND uses machine learning in the localization process
Thanks to the rise of neural networks, machine translations are more reliable than ever before. However, they’re far from perfect, meaning they’ll always be a place for human translators. Looking for the best of both worlds? At BLEND, we combine the automation of AI-powered machine translation with an expert team of human linguists to deliver first-class results, every time.
We use NMTPE (neural machine translation with post-editing) to deliver accurate results that are always on-brand and culturally relevant. You enjoy the cost and time-saving benefits of automation, safe in the knowledge that your content has been successfully localized by native speakers with industry-leading expertise.
Thanks to embedded technology, our global network of linguists can easily connect with your workflows, simplifying the localization process. What’s more, we’re the ideal localization provider to help you with your expansion plans. With thousands of linguists working in over 120 languages, we’re the go-to localization partner for brands looking to scale and expand into key markets.
Ready to learn more about how BLEND can help you with the localization process? Get in touch with the team today.
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