A Richly Annotated, Multilingual Parallel Corpus for Hybrid Machine Translation

Eleftherios Avramidis, Marta R. Costa-jussa, Christian Federmann, Josef van Genabith, Maite Melero, Pavel Pecina

In: 8th ELRA Conference on Language Resources and Evaluation. International Conference on Language Resources and Evaluation (LREC-12) May 23-25 Istanbul Turkey European Language Resources Association (ELRA) 5/2012.


In recent years, machine translation (MT) research has focused on investigating how hybrid machine translation as well as system combination approaches can be designed so that the resulting hybrid translations show an improvement over the individual “component” translations. As a first step towards achieving this objective we have developed a parallel corpus with source text and the corresponding translation output from a number of machine translation engines, annotated with metadata information, capturing aspects of the translation process performed by the different MT systems. This corpus aims to serve as a basic resource for further research on whether hybrid machine translation algorithms and system combination techniques can benefit from additional (linguistically motivated, decoding, and runtime) information provided by the different systems involved. In this paper, we describe the annotated corpus we have created. We provide an overview on the component MT systems and the XLIFF-based annotation format we have developed. We also report on first experiments with the ML4HMT corpus data.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence