Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation. Over the last few decades, multiple efforts have been undertaken to investigate incorrect translations caused by the polysemous nature of words. Within this body of research, some studies have posited that models pick up semantic biases existing in the training data, thus producing translation errors. In this paper, we present DiBiMT, the first entirely manually-curated evaluation benchmark which enables an extensive study of semantic biases in Machine Translation of nominal and verbal words in five different language combinations, namely, English and one or other of the following languages: Chinese, German, Italian, Russian and Spanish. Furthermore, we test state-of-the-art Machine Translation systems, both commercial and non-commercial ones, against our new test bed and provide a thorough statistical and linguistic analysis of the results. We release DiBiMT at https://nlp.uniroma1.it/dibimt as a closed benchmark with a public leaderboard.

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.

This work is based on the testing of a remote interpreting (RI) delivery platform conducted a year before the disruptive COVID-19 pandemic outbreak, and aimed at assessing the use and experience of such systems in a university setting. A survey was administered to the different groups of users (interpreters, audience, and speakers) involved in two tests to collect their responses and remarks, and assess trends and perceptions in their experience. According to emerging findings of the research project, RI was already considered to be an indisputable yet burgeoning resource for conference settings with potential convenience and benefits for each group of users. However, participants’ remarks early suggested that all the parties involved in the industry need to collaborate to effectively improve and enhance such services. Specific training on RI modalities would also appear to be increasingly necessary for interpreters to adapt to new raising working conditions and meet a thriving demand—and training institutions would ever more have to offer adequate solutions, while this technological shift also requires receptiveness and adaptability to an abruptly diversifying and evolving profession.