Quantity and quality of input affect language development, but input features also depend on the context of language emission. Previous research has described mother-child interactions and their impact on language development according to activity types like mealtimes, book reading, and free play. Nevertheless, few studies have sought to quantify activity types in naturalistic datasets including less-studied languages and cultures. Our research questions are the following: we ask whether regularities emerge in the distribution of activity types across languages and recordings, and whether activities have an impact on mothers' linguistic productions. We analyse input for two children per language, at three developmental levels. We distinguish three activity types: solitary, social and maintenance activities, and measure mothers' linguistic productions within each type. Video-recorded activities differ across families and developmental levels. Linguistic features of child-directed speech (CDS) also vary across activities – notably for measures of diversity and complexity – which points to complex interactions between activity and language.

Little is known about what guides children in their acquisition of grammatical categories. This paper investigates how semantic knowledge could be involved in discovering these categories, thus confronting two competing hypotheses: are semantic categories innate, or are they developed in a piecemeal fashion? We tested for regular associations between basic semantic dimensions and the development of the founding categories of noun and verb. Six perceptually based semantic dimensions (Parisse and Poulain, 2010), shared by nouns and verbs but potentially distinctive, are coded in the productions of three children aged 1;06 to 2;06. Our results suggest that semantic dimensions do not offer an entry into the early differentiation of noun and verb categories.

While translation technology is now at the core of most translator training programmes, only a handful include teaching of statistical machine translation (SMT). This paper reports on the design and evaluation of an SMT course which we introduced in the second year of our Master’s degree in Multilingual Specialised Translation in 2016.

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.