No abstract

The aim of the MultiTraiNMT Erasmus+ project is to develop an open innovative syllabus in neural machine translation (NMT) for language learners and translators as multilingual citizens. Machine translation is seen as a resource to provide support to citizens when trying to acquire and develop language skills, provided they are given informed and critical training. Machine translation would thus help tackle the mismatch between the EU aim of having multilingual citizens who speak at least two foreign languages and the current situation in which they generally fall far short of this objective. The training materials consist of an open-access coursebook, an open-source NMT web application (MutNMT) for training purposes and corresponding activities.

Machine Translation (MT) is one of the oldest language technologies having been researched for more than 70 years. However, it is only during the last decade that it has been widely accepted by the general public, to the point where in many cases it has become an indispensable tool for the global community, supporting communication between nations and lowering language barriers. Still, there remain major gaps in the technology that need addressing before it can be successfully a0146pplied in under-resourced settings, can understand context and use world knowledge. This chapter provides an overview of the current state-of-the-art in the field of MT, offers technical and scientific forecasting for 2030, and provides recommendations for the advancement of MT as a critical technology if the goal of digital language equality in Europe is to be achieved.

We present a multilingual preordering component tailored for a commercial Statistical Machine translation platform. In commercial settings, issues such as processing speed as well as the ability to adapt models to the customers’ needs play a significant role and have a big impact on the choice of approaches that are added to the custom pipeline to deal with specific problems such as long-range reorderings.We developed a fast and customisable preordering component, also available as an open-source tool, which comes along with a generic implementation that is restricted neither to the translation platform nor to the Machine Translation paradigm. We test preordering on three language pairs: English →Japanese/German/Chinese for both Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). Our experiments confirm previously reported improvements in the SMT output when the models are trained on preordered data, but they also show that preordering does not improve NMT.