Digital Developments in Translation and Interpreting
In today’s era, every enterprise is constantly seeking to expand their horizons in the communications industry. The pressure to attract a global clientele has significantly increased the need to communicate across diverse languages and cultures. Cross-context communications have evolved through the aid of technology and the introduction of a range of different tools, creating new means of communicating within the media and advertising sector.
In this increasingly interconnected world, however, a bad translation can also have greater financial repercussions than ever. In some cases, a slight miscommunication can lead to a loss of reputation, physical harm, and even corporate catastrophes. To learn how to avoid such disasters, Tech Plugged speaks to Matteo Ippoliti, the Italian-born, Dubai-based founder of Langpros – The Language Professionals about how the translation industry has evolved and what can be some crucial expectations moving forward.
How has AI evolved in the industry?
Artificial Intelligence has evolved in many different ways in recent years, thanks to progress in the field of machine learning, in which computers are programmed to learn from experience and respond within certain parameters. This has led to a significant rise in domestic digitalization, with AI home assistants such as Amazon Echo becoming ubiquitous, for example. People are getting more comfortable using AI-programmed applications and software, which was perhaps not the case previously. In the translation industry in particular, AI has managed to break the language barrier. People are no longer as hesitant to travel to a place where they may find it difficult to communicate.
Computer-Assisted Translation (CAT) tools have been in use for at least two decades. In addition to more and more advanced CAT tools which help human translators manage terminology and improve consistency across translations, new software is able to generate translations entirely automatically. These machine translations are then revised by human translators, whose role becomes closer to that of an editor.
In 2019, Google developed a new type of machine translation called neural machine translation, which achieved new levels of accuracy. This is especially true for technical and legal texts, where the terminology may be complex but the language is very plain and simple, requiring only minimal refinement by human translators. However, it does not yet work quite as effectively for marketing translations or other more creative texts.
Additionally, Facebook completely changed its outlook and introduced AI as its primary method of communication. While its use was previously limited to certain situations, now even social media have begun to rely on AI to create contextually appropriate translations.
Although Machine Translation could be used for interpreting as well, combined with speech recognition, text-to-text translation, and speech synthesis (similar to what you actually experience nowadays with Alexa, Siri, or Google) the results are still not satisfactory for most of the instances where interpreting is required (i.e. international conferences and congresses). AI has certainly improved the overall Machine translation quality but it is still very weak at thinking and analyzing context and nuances, especially for events where humans interact using different sensory channels. As a matter of fact, tonality, accents, gestures, and non-verbal language, in general, can play a very important role in communication, sometimes even more important than verbal language.