The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation

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

In: Proceedings of the 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.


We describe the Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT). Participants of the challenge are requested to build hybrid translations by combining the output of several MT systems of different types. We first describe the ML4HMT corpus used in the shared task, then explain the XLIFF-based annotation format we have designed for it, and briefly summarize the participating systems. Using both automated metrics scores and extensive manual evaluation, we discuss the individual performance of the various systems. An interesting result from the shared task is the fact that we were able to observe different systems winning according to the automated metrics scores when compared to the results from the manual evaluation. We conclude by summarising the first edition of the challenge and by giving an outlook to future work.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence