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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 dimensionality equal to the number of training subcorpora; each entry in the(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)
State of the art phrase-based statistical machine translation systems typically contain two features which estimate the “forward” and “backward” conditional translation probabilities for a given pair of source and target phrase. These two “relative frequency” (RF) features are derived from three counts: the joint count of the source and target phrase and(More)
This paper proposes a new automatic machine translation evaluation metric: AMBER, which is based on the metric BLEU but incorporates recall, extra penalties, and some text processing variants. There is very little linguistic information in AMBER. We evaluate its system-level correlation and sentence-level consistency scores with human rankings from the WMT(More)
NRC’s Portage system participated in the English-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 investigates the combination of word-alignments computed with the competitive linking algorithm and well-established IBM models. New training methods for phrase-based statistical translation are proposed, which have been evaluated on a popular traveling domain task, with English as target language, and Chinese, Japanese, Arabic and Italian as(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 phrasebased SMT. In this paper, we demonstrate that mixture model adaptation of a lexicalized RM(More)
Many machine translation (MT) evaluation metrics have been shown to correlate better with human judgment than BLEU. In principle, tuning on these metrics should yield better systems than tuning on BLEU. However, due to issues such as speed, requirements for linguistic resources, and optimization difficulty, they have not been widely adopted for tuning. This(More)
In this paper, we describe the system and approach used by Institute for Infocomm Research (IR) for the IWSLT 2007 spoken language evaluation campaign. A multi-pass approach is exploited to generate and select best translation. First, we use two decoders namely the open source Moses and an in-home syntax-based decoder to generate N-best lists. Next we spawn(More)