DGT-TM: A freely available Translation Memory in 22 languages

By Ralf Steinberger, Andreas Eisele, Szymon Klocek, Spyridon Pilos, Patrick Schlüter. 2012

162 citations recorded by the source corpus.

The European Commission's (EC) Directorate General for Translation, together with the EC's Joint Research Centre, is making available a large translation memory (TM; i.e. sentences and their professionally produced translations) covering twenty-two official European Union (EU) languages and their 231 language pairs. Such a resource is typically used by translation professionals in combination with TM software to improve speed and consistency of their translations. However, this resource has also many uses for translation studies and for language technology applications, including Statistical Machine Translation (SMT), terminology extraction, Named Entity Recognition (NER), multilingual classification and clustering, and many more. In this reference paper for DGT-TM, we introduce this new resource, provide statistics regarding its size, and explain how it was produced and how to use it.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Simple and Effective Retrieve-Edit-Rerank Text Generation (2020, methodology): “For machine translation, Bulte and Tezcan (2019) developed a retrieve-and-edit based LSTM model called Neural Fuzzy Repair (NFR), which they applied on two MT datasets obtained from (Steinberger et al., 2012).”
Exploring the Planet of the APEs: a Comparative Study of State-of-the-art Methods for MT Automatic Post-Editing (2015, methodology): “We build our LMs from approximately 2.5M translations of the same English sentences collected from Europarl (Koehn, 2005), DGT-Translation Memory (Steinberger et al., 2012), JRC Acquis (Steinberger et al., 2006), OPUS IT (Tiedemann, ) and other Autodesk data common to all languages.”
Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation (2019, methodology): “Several configurations are tested on the DGT-TM data set (Steinberger et al., 2013) for the language directions English into Dutch (EN→NL) and English into Hungarian (EN→HU).”
A massively parallel corpus: the Bible in 100 languages (2014, background): “Steinberger et al. (2013) list a number of potential uses of parallel corpora in NLP.”
Billions of Parallel Words for Free: Building and Using the EU Bookshop Corpus (2014, methodology): “In our experiment we built a phrase-based baseline English-Latvian SMT system using the DGT-TM corpus 10 (Steinberger et al., 2012) and the Moses SMT toolkit (Koehn et al., 2007), and evaluated it using a balanced evaluation set 11 containing legal texts, EU texts, news, fiction, business letters,…”