Machine Translation Training Data Research Articles

OUTLINE OF A DIDACTIC FRAMEWORK FOR COMBINED DATA LITERACY AND MACHINE TRANSLATION LITERACY TEACHING

This paper outlines a didactic framework for combined data literacy and machine translation (MT) literacy teaching for translation and specialised communication students. The framework is being developed in the context of the DataLitMT project, a publicly funded project at the Institute of Translation and Multilingual Communication at TH Köln – University of Applied Sciences, Germany, which develops didactic resources for teaching data literacy in its translation-specific form of MT literacy to students of BA and MA programmes in translation and specialised communication studies. After discussing the high relevance of machine trans-lation literacy and data literacy in professional translation contexts, the paper introduces the DataLitMT project and discusses a framework of professional MT literacy and an MT-oriented data literacy framework, which form the two theoretical pillars of the project. Also, the interface between MT literacy and data literacy will be illustrated by showing how specific (sub)dimensions of data literacy can be mapped to relevant (sub)dimensions of professional MT literacy. Finally, the paper presents some preliminary didactic resources of the DataLitMT project – concerned with social biases in MT, with MT training data preparation and with automatic MT quality evaluation – and discusses how these resources can be used to teach specific (sub)dimensions of data literacy and professional MT literacy to students in the fields of translation/specialised communication studies. Keywords: data literacy, professional machine translation literacy, neural machine translation, translation didactics, DataLitMT

MuST-C: A multilingual corpus for end-to-end speech translation

End-to-end spoken language translation (SLT) has recently gained popularity thanks to the advancement of sequence to sequence learning in its two parent tasks: automatic speech recognition (ASR) and machine translation (MT). However, research in the field has to confront with the scarcity of publicly available corpora to train data-hungry neural networks. Indeed, while traditional cascade solutions can build on sizable ASR and MT training data for a variety of languages, the available SLT corpora suitable for end-to-end training are few, typically small and of limited language coverage. We contribute to fill this gap by presenting MuST-C, a large and freely available Multilingual Speech Translation Corpus built from English TED Talks. Its unique features include: i) language coverage and diversity (from English into 14 languages from different families), ii) size (at least 237 hours of transcribed recordings per language, 430 on average), iii) variety of topics and speakers, and iv) data quality. Besides describing the corpus creation methodology and discussing the outcomes of empirical and manual quality evaluations, we present baseline results computed with strong systems on each language direction covered by MuST-C.