Technology-Augmented Multilingual Communication Models: New Interaction Paradigms, Shifts in the Language Services Industry, and Implications for Training Programs (2021)

Abstract: This paper explores how technology, particularly digital tools and artificial intelligence, are impacting multilingual communication and language transfer processes. Information and communication technologies are enabling novel interaction patterns, with computers transitioning from pure media to actual language generators, and profoundly reshaping the industry of language services, as the relevance of language data and assisting engines continues to rise. Since these changes deeply affect communication and languages models overall, they need to be addressed not only from the perspective of information technology or by business-driven companies, but also in the field of translation and interpreting studies, in a broader debate among scholars and practitioners, and when preparing educational programs for the training of specialised language professionals. Special focus is devoted to some of the latest advancements in automatic speech recognition and spoken translation, and how their applications in interpreting may push the boundaries of new ‘augmented’ real-world use cases. Hence, this work—at the intersection of theoretical investigation, professional practice, and instructional design—aims at offering an introductory overview of the current landscape and envisaging potential paths for forthcoming scenarios.

## language technologies information and digital technologies have had a profound impact on society and communication over the past decades and even more in the last few years. statistical and neural systems are at the foundation of many high-tech and 'intelligent' solutions employed in almost any domain nowadays, including language-in all its dimensions and areas of application. computers and devices process and analyse language data for several purposes (from text analysis and speech recognition to data mining and information retrieval) by applying the models of computational linguistics and natural language processing (nlp). today, applications of language technologies include automatic speech recognition (asr) systems providing dictation and transcription, voice assistants, chatbots, spelling and grammar checkers, writing assistants, speech synthesis, and interactive voice response (ivr) systems, just to name a few. however, research in the field is seldom public or shared since it often deals with trade secrets of the companies that hold such valuable technology and know-how, which they also leverage for the related remarkable commercial value. moreover, as it will be recalled later, publications on the topic are often confined to computer science (cs)-despite language technologies entailing a multi-and interdisciplinary approach by their very nature. ## renovating language programs and vocational training just like research on language technologies (and technology for language practitioners) needs to overcome the boundaries of cs to enter t&i studies too, the time has also come for training programs-both university degrees for aspiring linguists as well as vocational training and continuing professional development (cpd) courses-to systematically integrate all of this in the classrooms. to achieve the desired outcome, a holistic and integrated implementation approach is required. indeed, current challenges in the realization of such programs include-but are not limited to-the diverse backgrounds and expertise degrees of both trainees and trainers (since they are still typically formally trained in either one of the two environments) and the compelling necessity to design curricula in which technology is not a mere supplement segregated to specific courses, but rather an element underlying the structure of programs and a tool regularly available to trainees. ## future scenarios and final remarks the foundation for the introductory overview outlined in this work is considering language transfer processes as communication acts, rather than mere information or lexical correspondence. this is the reason for encouraging the inclusion of language technology studies within a wider communicative framework. text-based language technologies already significantly impacted human communication and human-machine interaction patterns, and written translation activities are extensively benefitting from numerous applications. these innovations have already had a critical impact on how communication is performed with regards to language solutions. for instance, search engine optimization (seo) has overturned how online content is conceived and put into words. machine translation (mt) too has influenced the way global content and texts addressed to international audiences are drafted, to such an extent that pre-editing has become common practice for globalization service providers. likewise, the long-term influence mt has even on the language used by translators and post-editors is worth further investigation. on the other hand, fast-paced improvements are also shedding a new light on voicebased language technologies, whose consideration is turning from accessibility to full productivity resources with other paradigm shifts on the horizon. just like language varies diaphasically depending on context, e.g. with 'baby talk' and 'foreigner talk', the same may presumably happen when communicating to computers by resorting to specific 'computer-' or 'machine talk'. since these technologies amplify diamesic variations in the use of language, it can be reasonably expected that speech addressed to machines will become different from spontaneous verbal expression. that could only be consolidated over time by society becoming accustomed to interacting with machines through voice, as well as an increasingly blurred dividing line between spoken and written language (due to the ubiquitous usage of mobile devices, chats, and voice messages) and a machine-induced alignment to common cognitive structures representing the linguistic knowledge of speakers of any language @xcite . furthermore, text-based nlp applications like pre-filled responses or suggested writing hint at how much human communication relies upon automatic and perfunctory mechanisms, and how many interactions can truly be managed with little 'intelligence' or language understanding. still, it seems unlikely that communicators will completely adapt their speaking style to the outreach (and limits) of digital tools in some sort of pre-editing process of their talks. as conference speakers never adapted their rhetoric to the modality they were being interpreted with (e.g. simultaneous or consecutive), it will not happen with machine talk or computerassisted interpreting either. nonetheless, it is also true that real-world environments are becoming increasingly 'ai-friendly', i.e. ever more shaped around the abilities of computational artefacts @xcite . at first, it is far more likely-as practice with support tools already proves to practitioners who make use of them-that interpreters may alter and adjust their interpreting techniques to the performance and output of these resources and their prompts. albeit machine talk still looks distant from real-world use cases, computer(-assisted) interpreting talk could more reasonably be an emerging trend. after all, all communicative acts-just like all translations-are built around degrees of negotiation @xcite , in which communication is adjusted according to the behaviour of interlocutors, their use of language, their relationship, context, and levels of compromise. research in common sense ai is also trying to narrow the gap with in-context human language models (e.g. when deixis is in place) by studying new training methods that would enable technology to detect and exploit elements from the multimodal real world. 'vokenization', as a combination of visual and language training datasets, is one of the most interesting examples of this @xcite . visual-language models may produce astounding enhancements in robotic assistants or automated subtitling and dubbing, where both verbal and non-verbal traits play equivalent roles. despite the existing limits of language technologies and still high word error rate (wer) scores in the performance of asr dampen the enthusiasm towards silver bullet ai solutions, there are numerous operating resources not even specifically designed for translators or interpreters (like multilingual semantic networks and knowledge graphs, or named entity recognition and terminology extraction tools) which can turn out to be valuable assets @xcite . as linguists become acquainted with technological tools, proficiently learn to use them, and consequently improve their performance, further experimental assessments even on a remodelled and tailored version of @xcite popular testing for computer intelligence could be investigated to detect and observe the difference in outputs from language practitioners who make use of it support tools and those who do not. ultimately, technology should not be conceived as an impending threat aiming at replacing humans, but as a resource providing support to ingeniously achieve the best possible cooperation between human abilities and computational efficiency. this objective can be attained by thoroughly considering springing communication paradigms to bolster high-quality training data and valuable language resources (elrc, 2019) and, above all, by adequately educating practitioners for a critical, accountable, and transparent use of language technology.