Mixture-Modeling with Unsupervised Clusters for Domain Adaptation in Statistical Machine Translation
نویسنده
چکیده
In Statistical Machine Translation, in-domain and out-of-domain training data are not always clearly delineated. This paper investigates how we can still use mixture-modeling techniques for domain adaptation in such cases. We apply unsupervised clustering methods to split the original training set, and then use mixture-modeling techniques to build a model adapted to a given target domain. We show that this approach improves performance over an unadapted baseline, and several alternative domain adaptation methods. Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-62826 Published Version Originally published at: Sennrich, Rico (2012). Mixture-modeling with unsupervised clusters for domain adaptation in statistical machine translation. In: 16th EAMT Conference, Trento, Italy, 28 May 2012 29 May 2012, 185-192. Mixture-Modeling with Unsupervised Clusters for Domain Adaptation in Statistical Machine Translation Rico Sennrich Institute of Computational Linguistics University of Zurich Binzmühlestr. 14 CH-8050 Zürich
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