In a rapidly changing scenario, translation professionals need to evolve in order to face new challenges and needs. Without a doubt, technology emerges as a key ally in the world of translation.
What are the new translation technologies and how have they developed?
Evolution in the Face of a New Context
Although there is a history dating back to the 9th century with the Al-Kindi Manuscript, it was with the advent of computers in the mid-20th century that translation technology began to take shape. Several milestones have marked this evolution, such as the Georgetown-IBM Experiment from 1950, the rise of Statistical Automatic Translation (SMT) between 1980 and 1990, the use of commercial Computer-Assisted Translation (CAT) tools, the first cloud-based Translation Management Systems and the emergence of Neural Automatic Translation, among others.
The truth is that, as in other areas, this technological evolution in the linguistic industry was linked to new global needs and scenarios. A team of professionals from Dublin City University and University College London studied the role of translation and interpretation in crisis response, as well as the role of these services as a risk reduction tool in the disaster management cycle.
Experts note that, in today’s era of globalization, greater urbanization, and migration, communication before, during, and after a crisis of any kind must be multilingual and multicultural. This communication is made possible through translation and interpretation. “Professional translators and interpreters are key players in crisis communication,” explains Sharon O’Brien, associate dean of research at Dublin City University’s Faculty of Humanities and Social Sciences.
In this context, automatic translation can now be seen as a great help in responding to crises given the production speed it allows and its availability in an increasing number of languages.
However, the use of automatic translation has limits technically, operationally and even ethically speaking, which include the lack of large linguistic data for translation engines to build upon, and the lack of coverage for languages that may be required in particular situations.
The Future of Translation
Beyond crisis scenarios, translation technology is always evolving to adapt to new demands. In addition to machine translation software (RBMT, SMT, NMT), there are a number of tools that have been positioning themselves over the past decade.
Thus, when choosing a language services partner, have in mind that leading organizations tend to think of technology in hybrid terms. In addition to the expertise of their translators and the certifications they might have, it is important to understand how the potential partner chooses to engage with translation technology.
A technology-savvy translation provider usually employs CAT tools to ensure consistency, high productivity, and cost savings. Furthermore, artificial intelligence and automated translation engines are tools that can provide quick, less expensive results, although usually of a lower quality. It is always essential to balance the benefits and risks implied by the use of each tool.
The value of the NLP (Natural Language Processing) translation market will have an annual growth rate (CAGR 2023-2030) of 13.48%, resulting in a market volume of USD 11.94 billion by 2030.
On another note, when we talk about multilanguage translations, whose use is demanded by today’s globalization, the focus is primarily placed on simplicity and security appears to be of extreme importance. In that sense, Translation Management Systems (TMS) have consolidated as a key ally for companies seeking global growth.
As TMS allow for streamlining all processes and delivery times, among other advantages, they bring scalability to translation projects. In addition, these systems have features integrated with CAT tools and can process machine translation outputs.
Finally, it is clear that artificial intelligence is among the most groundbreaking developments in translation technology. While such advancements still present many challenges, the truth is that the relationship between linguistics and AI can be very beneficial: for example, AI helps improve the processing capabilities of natural language. And an artificial intelligence system has even been developed that can learn the rules and patterns of human languages on its own.
Multiple developments tend to provide increasingly personalized and intuitive solutions, nurturing the knowledge and experience of human translators. The key to achieving an optimal result is to clearly understand what our goal is and to have a partner who can provide us with the most appropriate process, whether it is 100% automatic, hybrid or fully human translation.