Abstract
It is generally accepted that the performance of a statistical machine translation (SMT) system depends significantly on the concordance between the domain of training and test data. During the last years several methods have been proposed in order to deal with out- of-domain words. Less to no attention has been paid however to text genre within the same domain. In this paper we demonstrate that the style of the training corpus may influence the quality of the translation output even when the domain of the training and test data remains al- most unchanged, but the text genre changes. We use as training data the JRC-Acquis and as test data the Europarl corpus. We include also experiments with an out-of-domain test data, as comparison for the variation of performance of the SMT system.
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Notes
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The tag < p > from the initial HTML files.
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In the Moses description, all sentences longer than forty tokens are excluded.
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Status: February 2011; http://www.statmt.org/europarl/
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A one-to-one comparison is not possible, as the training and test data are not the same.
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Word-form = Declination form, conjugation form, etc.
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Acknowledgments
Part of the work in this paper was part of the EU-Project ATLAS, supported through the ICT-PSP-Programme of the EU-Commission (Topic “Multilingual Web”) and the PhD research conducted by Monica Gavrila at the University of Hamburg (see [4]).
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Gavrila, M., Vertan, C. (2014). Text Genre – An Unexplored Parameter in Statistical Machine Translation. In: Vetulani, Z., Mariani, J. (eds) Human Language Technology Challenges for Computer Science and Linguistics. LTC 2011. Lecture Notes in Computer Science(), vol 8387. Springer, Cham. https://doi.org/10.1007/978-3-319-08958-4_37
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