Journal of Translation Studies vol. 01/2021, pp. 87–108 © 2021 François Massion - DOI https://doi.org/10.3726/JTS012021.6 FRANÇOIS MASSION D.O.G. Dokumentation ohne Grenzen GmbH [email protected] Terminology in the Age of Artificial Intelligence Abstract Until a few years ago, artificial intelligence (AI) played no role in practical terminology work. Meanwhile, intelligent terminology management systems have become part of the tool landscape. They model knowledge by building concept networks with the help of rela- tions. These intelligent terminologies can help improve the performance and quality of AI- based applications and support translators and interpreters in their daily work. Intelligent terminology repositories offer challenging new opportunities for translators, interpreters and knowledge workers. This article gives some background information and explains the functioning of intelligent terminologies. Keywords artificial intelligence, machine translation, terminology, semantic frame, machine cognition 1. Introduction Terminology has always been an integral part of the work of translators or interpreters. To a large extent, the challenge of translation amounts to understanding the meaning of special terms and finding their equivalents in the target language. We have recently seen with Brexit how difficult it has been for the various parties involved to communicate effectively without sharing a Journal of Translation Studies vol. 01/2021 - This work is licensed under a Creative Commons CC-BY 4.0 license. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ 88 François Massion common understanding of the term Brexit (or associated terms like backstop or hard border, etc.). This is representative of many situations experienced by language professionals. As terms play a key role in translation and interpretation and in communication, many organizations, companies or individuals have built multilingual terminologies that they store in different formats, from simple Excel file to complex relational databases. With the rise of AI, some of the established ideas on terminology are now under review. 2. The practice of terminology today Terminology work has a long tradition. The Austrian Engineer Eugen Wüster (1898-1977) laid the foundation of modern terminology work in the early 1930s. The industrialization of society and the technological revoluti- ons had a profound impact on language, especially on language for special purpose (LSP) and Wüster took an active part in its shaping. His doctoral dissertation Internationale Sprachnormung in der Technik, besonders in der Elektrotechnik (“International Language standardization in technology, particularly in electrical engineering”) became a standard work in applied linguistics (Picht and Schmitz 2001). Wüster later published numerous artic- les and books on how to organize terminology. Wüster’s ideas laid the basis of practical prescriptive terminology work. His approach is pretty much in line with the need of industrial companies to standardize the use of terms, especially of technical terms in order to make communication more efficient. Of course, prescriptive terminology work is linked to a special purpose and therefore, Wüster’s views were bound to draw some criticism. Scholars from different fields pointed at socio-cognitive or communication aspects to underline the weakness of Wüster’s model in explaining how terminology is actually perceived and used in society. For example, the “the theory of doors” of Teresa Cabré (Cabré 2000) or the socioterminology theory of François Gaudin (Gaudin 1990). Terminology in the Age of AI 89 So far, the ideas of Wüster’s critics have had little impact on the terminology repositories used by professional translators or interpreters. Most terminology repositories in the industry are concept-based. This means that for each language all terms designing a concept are gathered and recorded together with attributes and definitions. In many cases these repositories contain usage recommendations, i.e. they display allowed and prohibited terms. The reason for this approach can easily be understood from a practical point of view knowing e.g. that large industrial companies or organizations frequently record numerous terms for the same part (car phone, car telephone, cell phone, cellular phone, mobile phone, etc.) and want to standardize their corporate language. 3. AI disruption As in many other branches, new technologies bring along major disruptions and force all participants to review their own role as well as accepted ideas. This is the case with the growing influence of artificial intelligence in all areas related to natural language. AI as such is not new. The term Artifi- cial Intelligence (AI) was coined more than 60 years ago at the Dartmouth conference in 1956 and there have since been many AI projects related to knowledge and language. Just to name a few, there were early attempts to automatically translate documents, starting in 1946. There was the ELI- ZA program designed by Weizenbaum in 1965 that could communicate in natural language with humans and pass the Turing test; or the first knowledge-based system (Dendral) built in 1965 by Edward Feigenbaum, Bruce G. Buchanan, Joshua Lederberg and Carl Djerassi. This system used knowledge bases and reasoning. Several AI pioneers tackled early on the task of modelling human thinking for computers, one prominent example being the work of Marvin Minsky (Minsky 1974) with his frame theory. He saw frames as the repre- sentation of a stereotyped situation that is called up by the brain when we encounter a new situation (Minsky 2000). 90 François Massion Fig. 1: Hierarchy of representations proposed by M. Minsky (from Minsky 2000) But all these developments took place in a parallel world, far away from the daily routine of interpreters and translators. As it is too often the case, sciences thrive in silos and sometimes research is done independently and in parallel on similar subjects. Now AI has reached the radar screen of translators, interpreters or language professionals. A turning point has been the widespread use of Deep Learning and Convolutional Networks since 2012 (Le Cun 2019) that boosted the performance of machine translation. Since then, most language professionals and scholars have been struggling to assess what effects AI will have on their profession. Artificial Intelligence has had a disruptive effect in nearly all fields where it is applied. And terminology is no exception. 4. The emergence of intelligent terminologies Until now the major purpose of terminology work has been to model and construct terminology repositories for use by humans. Whatever the prevailing theory was, its purpose has been to serve humans. The task of modelling knowledge for machines had been tackled separately by other disciplines like computer science with research areas such as logic (building Terminology in the Age of AI 91 up on the early work of Gottlob Frege and Bertrand Russell1) and ontologies (Willard Quine 2). AI led to the introduction of new types of terminology repositories into the traditional tool landscape. Intelligent terminologies a.k.a. ontotermino- logies, knowledge- rich terminologies, relational terminologies, termonto- logies are right now available for translators and interpreters. Intelligent terminologies belong to the family of augmented translation technologies that are inspired or driven by artificial intelligence. They model knowledge by creating multilingual conceptual networks using relationships. Like other technologies and concepts such as AI or machine translation, the idea is not new. Ontoterminology is a term coined by Roche in 2007 and further developed (Roche 2007; Roche et al. 2011) and termontology or termontography can be traced back to Temmerman and Kerremans in 2003 (Temmerman and Kerremans 2003; Kerremans and Temmerman 2004). Besides, the idea of connecting concepts has been formulated long ago. It can be found in Ross Quillian’s Ph.D. thesis in 1966 that introduced the idea of semantic networks (Quillian 1968). The difference now is that these ideas have become part of practical applications that are available to be used today on a wide scale. A look at WIPO Pearl3, BABELNET4 or tools like LOOKUP5 can confirm this. 5. The traditional model of terminology under scrutiny In order to better understand what these intelligent terminologies are, how they are structured and why the need for them has arisen, let’s first have a look at the weaknesses of the existing models. 1 Gottlob Frege published his Begriffsschrift, eine der arithmetischen nachgebildete Formelsprache des reinen Denkens in 1879. As the title says, he developed a formula language of pure thinking, based on arithmetic. Bertrand Russell further developed Frege’s ideas with a critical eye. In his book Introduction to Mathematical Philosophy (1919), Russell develops his ideas on logic. 2 Willard Quine worked in several fields including logic, the philosophy of language and ontology. 3 See https://wipopearl.wipo.int/en/linguistic. 4 See https://babelnet.org/. 5 See https://www.dog-gmbh.de/. 92 François Massion 5.1 The semiotic triangle Traditional terminologies are concept-based, i.e. they start from an abstract concept and collect for each language respectively all the terms (words, abbreviations or phrases) that designate that concept. The theoretical foun- dation of this approach is the semiotic triangle of reference, as first published by Ogden and Richards