Mixture-Modeling with Unsupervised Clusters for Domain Adaptation in Statistical Machine Translation Rico

@inproceedings{Sennrich2012MixtureModelingWU,
  title={Mixture-Modeling with Unsupervised Clusters for Domain Adaptation in Statistical Machine Translation Rico},
  author={Rico Sennrich},
  year={2012}
}
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… CONTINUE READING

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