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This paper proposes a new approach to domain adaptation in statistical machine translation (SMT) based on a vector space model (VSM). The general idea is first to create a vector profile for the in-domain development (" dev ") set. This profile might, for instance, be a vector with a di-mensionality equal to the number of training subcorpora; each entry in(More)
BLEU is the de facto standard machine translation (MT) evaluation metric. However , because BLEU computes a geometric mean of n-gram precisions, it often correlates poorly with human judgment on the sentence-level. Therefore , several smoothing techniques have been proposed. This paper systematically compares 7 smoothing techniques for sentence-level BLEU.(More)
A recent paper described a new machine translation evaluation metric, AMBER. This paper describes two changes to AMBER. The first one is incorporation of a new ordering penalty; the second one is the use of the downhill simplex algorithm to tune the weights for the components of AMBER. We tested the impact of the two changes, using data from the WMT metrics(More)
Previous research on domain adaptation (DA) for statistical machine translation (SMT) has mainly focused on the translation model (TM) and the language model (LM). To the best of our knowledge, there is no previous work on reordering model (RM) adaptation for phrase-based SMT. In this paper, we demonstrate that mixture model adaptation of a lexical-ized RM(More)
NRC's Portage system participated in the Eng-lish-French (E-F) and French-English (F-E) translation tasks of the ACL WMT 2010 evaluation. The most notable improvement over earlier versions of Portage is an efficient implementation of lattice MERT. While Portage has typically performed well in Chinese to English MT evaluations, most recently in the NIST09(More)
This paper studies three techniques that improve the quality of N-best hypotheses through additional regeneration process. Unlike the multi-system consensus approach where multiple translation systems are used, our improvement is achieved through the expansion of the N-best hypotheses from a single system. We explore three different methods to implement the(More)
This paper describes the statistical machine translation system developed at ITC-irst for the evaluation campaign of the International Workshop on Spoken Language Translation 2005. The system exploits two search passes: the first pass is performed by a beam-search decoder which generates an n-best list of translations, the second by a simple re-scoring(More)