Despite the growing importance of data in translation, there is no data repository that equally meets the requirements of translation industry and academia alike. Therefore, we plan to develop a freely available, multilingual and expandable bank of translations and their source texts aligned at the sentence level. Special emphasis will be placed on the labelling of metadata that precisely describe the relations between translated texts and their originals. This metadata-centric approach gives users the opportunity to compile and download custom corpora on demand. Such a generalpurpose data repository may help to bridge the gap between translation theory and the language industry, including translation technology providers and NLP.

We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al., 2018) and attention-based Transformer (Vaswani et al., 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality through a carefully designed human evaluation on English-German and German-English language pairs, the latter being particularly sensitive to latency constraints. The evaluation results allow us to identify the strengths and shortcomings of each model when we shift to the online setup.

The freely available European Parliament Proceedings Parallel Corpus, or Europarl, is one of the largest multilingual corpora available to date. Surprisingly, bibliometric analyses show that it has hardly been used in translation studies. Its low impact in translation studies may partly be attributed to the fact that the Europarl corpus is distributed in a format that largely disregards the needs of translation research. In order to make the wealth of linguistic data from Europarl easily and readily available to the translation studies community, the toolkit 'EuroparlExtract' has been developed. With the toolkit, comparable and parallel corpora tailored to the requirements of translation research can be extracted from Europarl on demand. Both the toolkit and the extracted corpora are distributed under open licenses. The free availability is to avoid the duplication of effort in corpus-based translation studies and to ensure the sustainability of data reuse. Thus, EuroparlExtract is a contribution to satisfy the growing demand for translation-oriented corpora.

Despite its importance in a globalized world, indirect translation is a peripheral and under-researched topic in translation studies. Existing research on indirect translation is almost exclusively limited to literary translation and focuses mainly on historical aspects. From a methodological perspective, textual analysis based on close reading is the main source of insight into indirect translation, while distant reading using computational approaches remains unexplored. In order to promote methodological innovation, this study gives a replicable demonstration of how to apply supervised machine learning to corpora of indirect translations. The study is based on comparable corpora of proceedings from the European Parliament. Open-access data is used to ensure the replicability of the proposed methodology. Based on the computational findings, the methodological caveats of this approach are discussed.

The Wikipedia category system was designed to enable browsing and navigation of Wikipedia. It is also a useful resource for knowledge organisation and document indexing, especially using automatic approaches. However, it has received little attention as a resource for manual indexing. In this article, a hierarchical taxonomy of three-level depth is extracted from the Wikipedia category system. The resulting taxonomy is explored as a lightweight alternative to expert-created knowledge organisation systems (e.g. library classification systems) for the manual labelling of open-domain text corpora. Combining quantitative and qualitative data from a crowd-based text labelling study, the validity of the taxonomy is tested and the results quantified in terms of interrater agreement. While the usefulness of the Wikipedia category system for automatic document indexing is documented in the pertinent literature, our results suggest that at least the taxonomy we derived from it is not a valid instrument for manual subject matter labelling of open-domain text corpora.