@inproceedings{federmann-etal-2012-ml4hmt,
title = "The {ML}4{HMT} Workshop on Optimising the Division of Labour in Hybrid Machine Translation",
author = "Federmann, Christian and
Avramidis, Eleftherios and
Costa-juss{\`a}, Marta R. and
van Genabith, Josef and
Melero, Maite and
Pecina, Pavel",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/996_Paper.pdf",
pages = "3430--3435",
abstract = "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.",
}
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%0 Conference Proceedings
%T The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation
%A Federmann, Christian
%A Avramidis, Eleftherios
%A Costa-jussà, Marta R.
%A van Genabith, Josef
%A Melero, Maite
%A Pecina, Pavel
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Doğan, Mehmet Uğur
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 May
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F federmann-etal-2012-ml4hmt
%X 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.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/996_Paper.pdf
%P 3430-3435
Markdown (Informal)
[The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation](http://www.lrec-conf.org/proceedings/lrec2012/pdf/996_Paper.pdf) (Federmann et al., LREC 2012)
ACL