Machine translation is opening opportunities for entrepreneurs to reach target audiences around the globe. Countless industries have already taken advantage of its benefits, most notably tech startups, law firms, medical institutions, and content publishers.
But they’re not the only ones that stand to benefit from the recent developments in machine translation. So, what is machine translation anyway? When did machines start translating languages? Why does it matter to you now? And what about the future?
Machine translation allows a computer to interpret and translate text from one language to another. Modern systems rely on natural language processing, a branch of AI that helps computers understand human language.
Thanks to advances in AI and neural network technology, machine translation has come a long way in recent years. Today, it offers businesses a way to translate large volumes of data without huge price tags.
RBMT relies on grammatical rules and dictionaries to generate translations. It is most useful for simple straightforward texts with basic meanings since it requires significant post-editing by human translators.
SMT is a method of machine translation that relies on statistics and data to generate translations. However, this system requires large amounts of data to look for patterns and correlations between the languages.
Hybrid machine translation combines the best features of statistical and rule-based machine translation. Translators first use statistical machine translation to generate a rough draft of the text. They then edit the draft using rule-based methods to improve accuracy.
Neural machine translation is a type of artificial intelligence that is used to translate text from one language to another. It uses a neural network to learn the rules of grammar and syntax for both languages in order to understand the context of the text and produce an accurate translation.
In the past, machine translation was often inaccurate and produced text that was difficult to understand. However, modern machine translation systems are much more accurate and can produce translations that are almost indistinguishable from those produced by human translators.
From just 260 words to billions per day, the inventors of machine translation could have never imagined expansion and commercial use of today’s level in the 1950s. However, it is impossible to recognize the value of these numbers without knowing the history.
Let’s explore the history of machine language translation to get a better understanding of how it works and perhaps, a deeper appreciation for how far we’ve come.
On January 8, 1954, the headline of IBM press release read: “Russian was translated into English by an electronic "brain" today for the first time.” The release went on to detail the first rules-based machine translation system developed by IBM.
Using six linguistic rules, it was able to translate 260 words between Russian and English and caused quite the sensation at the time. However, this computer translation was far from perfect.
The early machines were not viable. Researchers had to come up with extensive dictionaries as well as rules for structure & transformation. Still, it often produced inaccurate translations because it was impossible to come up with every rule.
In the 1980’s, a research team at IBM collected audio recordings and attempted to make developments in the field of speech recognition. However, the results of their efforts were not what they had hoped. So, they began applying statistical methods to machine translation.
Their research spawned more interest in statistical machine translation and formed the foundation for today’s top machine translation systems at Microsoft and Google.
In the 2000s, researchers worked to improve traditional statistical machine translation (SMT). Google Translate began as SMT but evolved into neural machine translation. Though the system has faced criticism for lack of transparency and biased translations, it has made efforts to address those issues while making significant contributions to NMT, which are discussed later in more detail.
Machine translation offers a number of advantages for your business, including cost savings, increased efficiency, and the ability to reach new markets. While there are still some challenges to be addressed, the accuracy of machine translation is constantly improving, making it an increasingly viable option for businesses of all sizes.
Machine translation can be much cheaper than human translation, especially when translating large volumes of text. If you partner with the right machine translation company, you can save more in project management costs as well.
For example, we offer testing to ensure your software is ready to launch and provides one year of continued support after delivery.
While human translators understand the nuances of language and bring a higher level of quality, they can take days or even weeks to translate large volumes of text. On the other hand, machine translation can translate thousands of words in minutes or hours.
The increased translation efficiency means corporations can get real value out of big data by implementing machine translation and post-editing services.
Even the best human translators make errors. Machine translators can improve the accuracy of human translation and vice versa, as they work together, the strengths of each complementing the other’s weaknesses.
While the digital world has globalized, consumers around the world have local mindsets, meaning what works for one community will not work in the next. Machine translation can help businesses expand their reach by allowing them to communicate with customers and partners in other languages.
“Because consumer needs vary around the world, the idea that a company can conquer a category with a one-size-fits-all strategy is a myth in most categories.”
Boston Consulting Group’s Center for Customer Insight (CCI)
Unless you have built a machine translation system of your own, you’ll need to choose a machine translation company to oversee the localization process. This decision is crucial to your success. To dive a bit deeper into localization, we spoke with a variety of MT experts at Tomedes, a leading provider of translation services.
If you’re a tech startup or business, whose aim is to globalize, these are some of the factors to look for in a company.
Make sure the provider offers high-quality translations. This is arguably the most important factor in choosing a machine translator. There's no point in using a provider that produces poor-quality translations – it will only reflect negatively on your business.
Does the provider have experience translating in your industry or domain? This is important to ensure that they are familiar with the terminology and concepts used in your content, especially if you’re in the tech, legal, or medical industries.
Check whether the provider offers human post-editing services. In many cases, it may be necessary to have a human edit your machine-translated content before it's published. This is particularly important for critical content (e.g., marketing materials, legal documents, etc.).
See if the provider offers any additional features that could be helpful for localization. For example, Tomedes offers glossaries and style guides that can be used to ensure your translations are consistent with your brand's voice.
Make sure the provider offers competitive pricing. While quality is important, you also need to conserve your resources for a successful localization. Be sure to compare prices from multiple providers before making your final decision.
Check the provider's customer reviews. This is a good way to get a sense of what others have thought about the quality of the provider's translations.
Does the provider offer any formal assurances for quality or delivery time, such as Tomedes 100% Quality Guarantee? This can give you peace of mind knowing that you'll be satisfied with their services.
One of the most significant trends in machine translation is the increasing use of neural networks. According to a recent article, “Lionbridge’s R&D teams estimate that Neural Machine Translation is improving by 3-7% every year."
As neural networks advance mimicking the way the human brain works, they'll prove to be very effective at translating more complex text, leading to further applications of neural machine translation.
Advances in AI machine translation will allow language companies to translate technical documents for specialized industries with less human editing. Data from social media and other online sources can be used to train machine translation systems.
Training improves the accuracy of specific types of translated texts, such as technical documents or legal documents. Over time, systems will move to automated translation as neural networks become large enough to reliably use automated processes.
Automated translation platforms will emerge, seeking to compete with Google’s Zero-Shot Machine Translation technology, which can automatically translate between multiple languages without any human intervention.
The recently unveiled technology is based on a new approach to machine translation that doesn't require the usual training data consisting of millions of examples of translated text. However, Zero-Shot Machine Translation is still in its infancy and requires human monitoring to ensure accuracy.
Of course, with the market’s focus on user experience, the language sector is no exception. Researchers are already working to make machine translation systems more user-friendly.
This includes developing easier to use systems and more helpful resources for users. For example, some machine translation systems now offer suggestions for alternative translations that might be more accurate.
We expect to see a continued focus on quality in the machine translation industry. With the rise of NMT and AI/ML, there’s an increased need for quality assurance (QA) methods and tools to help ensure that translations are accurate and meet the highest standards.
This shift is driven by a number of factors, including the need for more objective and reliable evaluations of machine translation quality, the increasing use of machine translation in commercial and mission-critical applications, and the growing availability of tools and resources for automatically assessing machine translation output.
Machine translation is not perfect, so we must address and acknowledge its limitations. One of the biggest challenges is dealing with idioms and colloquialisms, which can often be difficult for machines to understand. Then, there is always the issue of bias, which has been frequently cited in AI research. The solutions to these limitations, or rather how we respond to them will undoubtedly shape the future of machine translation and our global culture.
While many engage in the debate of humans versus machines, innovators have embraced technology as our future. They bring together the skills of translators and state-of-the-art technology to enhance their services. Here’s what they have to say about machines and translation:
"Lilt [aims to] build a solution that [will] combine the best of human ingenuity with machine efficiency."
• SPENCE GREEN, CEO of Lilt.
"Rather than taking the best that each can provide and blending them. The speed of machine translation is complemented by the unique touch that human translators bring to the table."
OFER TIROSH, CEO of Tomedes
“The world is evolving before our eyes and we must evolve with it. Like all conscientious businesses, we are using this moment in history to reflect on how we can grow.”
SCOTT W. KLEIN, CEO of LanguageLine Solutions
Without a doubt, machine translation will continue to grow in popularity and accuracy in the coming years. We move forward hoping to reach the dreams of the earliest researchers who hoped not just for advanced technology but for something more—“for the constructive and peaceful future of the planet.”
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