Translation technology has existed for more than 50 years, but as the world becomes ever more interconnected, it has become increasingly essential.
What is translation technology?
Translation technology means using software tools that support the process of converting written text from one language into another. Translation technology tools can increase productivity, accuracy and overall effectiveness.
The term “translation technology” covers everything from translation memories and terminology management tools to machine translation (MT), virtual interpreting technology and even speech-to-text technologies.
Before translation technology appeared, translation was done manually, with the help of printed dictionaries and the translator’s own intelligence.
The negative impact on business was substantial because of:
- Delayed time to market.
- An overall loss of consistency in content.
- The high cost of inefficient operations.
- Lower-quality results, as a consequence of manual error checking.
The arrival of translation technology changed everything. Tools such as translation memories – which store previous translations to inform and speed up future projects – and translation management systems help automate repetitive processes and increase control.
How does translation technology help business?
Today, translation technology tools offer companies:
- Guaranteed quality, through spelling and grammar checks.
- Improved operational efficiency by automating translation management tasks.
- Greater consistency of content, by storing previous translations that can be reused or consulted in future projects.
- A faster time to market, allowing more content to be produced in less time.
The history of translation technology
Some of the techniques used in modern translation technology go back to the 9th century, when the Arab cryptographer Al-Kindi developed a method of frequency analysis that is still used today. But it was only in the mid-20th century, when computers became available and affordable, that translation technology really began to take shape. Here is a brief journey through its past:
- The first machine translation system – 1950s. Launched by Georgetown University and IBM, the system was based on pre-programmed rules and dictionaries and, although it proved unreliable, it was still a step towards more advanced technology.
- The beginning of speech recognition technologies – 1970s. Launched by the US Department of Defense and the Advanced Research Projects Agency, they paved the way for voice-to-text technologies.
- The emergence of electronic dictionaries and terminology databases – 1980s. They made translation more accessible, giving translators instant access to information they could use during a project.
- The emergence of the precursors of modern translation management systems (TMS) – mid-1980s. Thanks to Coventry Lanchester Polytechnic and ALP System.
- The introduction of statistical machine translation by IBM – late 1980s to early 1990s. Word-based systems trained to translate from one language into another by comparing large amounts of parallel text in both languages.
- The emergence of the first widely used commercial computer-assisted translation (CAT) tools – early 1990s. A milestone that changed translation technology forever.
- The launch of a new version of IBM’s statistical translation engine – late 1990s. The system was based on phrases rather than words. It became the commercial standard for many years, until Google launched neural machine translation technology.
- The emergence of the first cloud-based TMS solutions – early 2000s. They allowed translation teams to work more flexibly and collaborate with colleagues regardless of location.
- The launch of Google Translate – 2006. The system that took the world by surprise first translated the input text into English before translating it into the target language. It used predictive algorithms that guessed which word should come next based on the words and phrases it had been trained on. But this guessing often led to grammatical errors.
- The launch of neural machine translation (NMT) by Google – 2016. It then became the new commercial standard in the field.
The main types of translation technology today
Computer-assisted translation (CAT) tools. Software applications created to support translators in their daily work. They use databases of previous translations as well as terminology, data segmentation and other resources that help the translation process.
Machine translation (MT). An automated system that produces translated content without human intervention.
Translation management systems (TMS). A software platform designed to manage large-scale translation projects. It streamlines the translation process from start to finish, creating a workflow from the initial request to project completion and allowing users to plan, assign and manage translation projects in an organized way.
The future of translation technology
Translation tools are already far more nuanced than they were just two decades ago, thanks to machine learning. Translation technology will only grow more powerful as artificial intelligence continues to evolve.
But translation will never be fully automatic, because human translators and specialized machine translation tools have complementary strengths: humans excel at creative, “outside the box” thinking, while AI is ideal for automating repetitive tasks.
As a result, experts agree that the future of translation will combine human intelligence with AI-based machine translation to produce the best results.
In the end, human translators and AI tools will work together as a single translation team, each playing a distinct role.
We will most likely move from computer-assisted human translation to human-assisted machine translation.
Post-editing of machine translation will ensure that AI-generated translations are accurate and fluent, while human translators make the final creative changes to suit a specific audience.
The future looks bright: translation technology will keep allowing translators to focus on more complex translation work, leaving tedious, repetitive tasks to AI-based tools.
The question is no longer whether to use translation technology, but “Which technology should we choose?”