Sonja Nießen

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In this paper we present a tool for the evaluation of translation quality. First, the typical requirements of such a tool in the framework of machine translation (MT) research are discussed. We define evaluation criteria which are more adequate than pure edit distance and we describe how the measurement along these quality criteria is performed(More)
In statistical machine translation, correspondences between the words in the source and the target language are learned from parallel corpora, and often little or no linguistic knowledge is used to structure the underlying models. In particular, existing statistical systems for machine translation often treat different inflected forms of the same lemma as(More)
In the framework of statistical machine translation (SMT), correspondences between the words in the source and the target language are learned from bilingual corpora on the basis of so-called alignment models. Among other things these are meant to capture the differences in word order in different languages. In this paper we show that SMT can take advantage(More)
S. Nie en, S. Vogel, H. Ney, and C. Tillmann Lehrstuhl f ur Informatik VI RWTH Aachen { University of Technology D-52056 Aachen, Germany Email: niessen@informatik.rwth-aachen.de Abstract We introduce a novel search algorithm for statistical machine translation based on dynamic programming (DP). During the search process two statistical knowledge sources(More)
In statistical machine translation, correspondences between the words in the source and the target language are learned from bilingual corpora on the basis of so called alignment models. Existing statistical systems for MT often treat different derivatives of the same lemma as if they were independent of each other. In this paper we argue that a better(More)
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