We address the problem of language model customization in applications where the ASR component needs to manage domain-specific terminology; although current state-of-the-art speech recognition technology provides excellent results for generic domains, the adaptation to specialized dictionaries or glossaries is still an open issue. In this work we present an approach for automatically selecting sentences, from a text corpus, that match, both semantically and morphologically, a glossary of terms (words or composite words) furnished by the user. The final goal is to rapidly adapt the language model of an hybrid ASR system with a limited amount of in-domain text data in order to successfully cope with the linguistic domain at hand; the vocabulary of the baseline model is expanded and tailored, reducing the resulting OOV rate. Data selection strategies based on shallow morphological seeds and semantic similarity via word2vec are introduced and discussed; the experimental setting consists in a simultaneous interpreting scenario, where ASRs in three languages are designed to recognize the domainspecific terms (i.e. dentistry). Results using different metrics (OOV rate, WER, precision and recall) show the effectiveness of the proposed techniques.

Language technologies, such as machine translation (MT), but also the application of artificial intelligence in general and an abundance of CAT tools and platforms have an increasing influence on the translation market. Human interaction with these technologies becomes ever more important as they impact translators’ workflows, work environments, and job profiles. Moreover, it has implications for translator training. One of the tasks that emerged with language technologies is post-editing (PE) where a human translator corrects raw machine translated output according to given guidelines and quality criteria (O’Brien, 2011: 197-198). Already widely used in several traditional translation settings, its use has come into focus in more creative processes such as literary translation and audiovisual translation (AVT) as well. With the integration of MT systems, the translation process should become more efficient. Both economic and cognitive processes are impacted and with it the necessary competences of all stakeholders involved change. In this paper, we want to describe the different potential job profiles and respective competences needed when post-editing subtitles.

We describe our experience with providing automatic simultaneous spoken language translation for an event with human interpreters. We provide a detailed overview of the systems we use, focusing on their interconnection and the issues it brings. We present our tools to monitor the pipeline and a web application to present the results of our SLT pipeline to the end users. Finally, we discuss various challenges we encountered, their possible solutions and we suggest improvements for future deployments.

With the increased audiovisualisation of communication, the need for live subtitles in multilingual events is more relevant than ever. In an attempt to automatise the process, we aim at exploring the feasibility of simultaneous speech translation (SimulST) for live subtitling. However, the word-for-word rate of generation of SimulST systems is not optimal for displaying the subtitles in a comprehensible and readable way. In this work, we adapt SimulST systems to predict subtitle breaks along with the translation. We then propose a display mode that exploits the predicted break structure by presenting the subtitles in scrolling lines. We compare our proposed mode with a display 1) word-for-word and 2) in blocks, in terms of reading speed and delay. Experiments on three language pairs (en→it, de, fr) show that scrolling lines is the only mode achieving an acceptable reading speed while keeping delay close to a 4-second threshold. We argue that simultaneous translation for readable live subtitles still faces challenges, the main one being poor translation quality, and propose directions for steering future research.

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.