B7: Modelling Human Translation with a Noisy Channel
Human translation is modelled on the basis of a noisy channel, as commonly done in machine translation. The two main objectives of translation, source language fidelity and target language conformity, are modelled probabilistically.
Different modes (interpreting, translation) and levels of expertise (learner, professional) are considered. The data set we use are translations of speeches from the EU Parliament which are compiled into a corpus. Computational translation models are built, which provide the basis for several studies on translationese, translation adequacy as well as translation complexity.
Analyzing variation in translation through neural semantic spaces Journal Article
Special topic: Neural Networks for Building and Using Comparable Corpora, Recent Advances in Natural Language Processing (RANLP), Varna, Bulgaria, 2019.
Detecting linguistic variation in translated vs. interpreted texts using relative entropy Inproceedings
Empirical Investigations in the Forms of Mediated Discourse at the European Parliament, Thematic Session at the 49th Poznań Linguistic Meeting (PLM2019), Poznan, 2019.
EuroParl-UdS: Preserving and Extending Metadata in Parliamentary Debates Inproceedings
ParlaCLARIN workshop, 11th Language Resources and Evaluation Conference (LREC2018), Miyazaki, Japan, 2018.
Exploring Variation in Translation with Relative Entropy Inproceedings
International Symposium on Parallel Corpora ECETT / PaCor 2018, Madrid, Spain, 2018.