Advances in Natural Language Processing (NLP) over the past decade have largely been driven by the scale of data and computation used to train large neural network-based models. However, these techniques are inapplicable to the vast majority of the world's languages, which lack the vast digitized text datasets available for English and a few other…

In order to translate and be translated, low-diffusion languages often use strategies that differ from those used by widely spoken languages and therefore create particular challenges for translators. One such strategy is indirect translation (including also relay interpreting). Since there are conflicting opinions about this practice within the…

The study focuses on literary translation between languages of low diffusion (LLD) from the perspective of a culture using such a language. The position of a language group in the world translation system determines the general flow of literary translation, but the specifics for the translation of an individual LLD may depend on a number of…

We present enetCollect, a large European network project funded as a COST Action that sets ground for combining crowdsourcing with IT technologies used in areas such as language learning and Natural Language Processing (NLP). This project tackles a major challenge of bringing together interdisciplinary researchers to foster language learning of…

Bilingual speakers must acquire the phonemic inventory of 2 languages and need to recognize spoken words cross-linguistically; a demanding job potentially made even more difficult due to dialectal variation, an intrinsic property of speech. The present work examines how bilinguals perceive second language (L2) accented speech and where…