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.

Publications

2019

Bizzoni, Yuri; Teich., Elke

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.

BibTeX

Karakanta, Alina; Menzel, Katrin; Przybyl, Heike; Teich, Elke

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.

BibTeX

2018

Karakanta, Alina; Vela, Mihaela; Teich, Elke

EuroParl-UdS: Preserving and Extending Metadata in Parliamentary Debates Inproceedings

ParlaCLARIN workshop, 11th Language Resources and Evaluation Conference (LREC2018), Miyazaki, Japan, 2018.

BibTeX

Karakanta, Alina; Przybyl, Heike; Teich, Elke

Exploring Variation in Translation with Relative Entropy Inproceedings

International Symposium on Parallel Corpora ECETT / PaCor 2018, Madrid, Spain, 2018.

BibTeX

Elke Teich

PI

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Tom Juzek

Postdoc

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Yuri Bizzoni

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Stefan Fischer

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Mihaela Vela

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Katrin Menzel

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Heike Przybyl

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