In this paper, we propose a novel syntactic based MT evaluation metric which only employs the dependency information in the source side. Experimental results show that our method achieves higher correlation with human judgments than BLEU, TER, HWCM and METEOR at both sentence and system level for all of the four language pairs in WMT 2010.

This article shows how the automatic disambiguation of discourse connectives can improve Statistical Machine Translation (SMT) from English to French. Connectives are firstly disambiguated in terms of the discourse relation they signal between segments. Several classifiers trained using syntactic and semantic features reach stateof-the-art performance, with F1 scores of 0.6 to 0.8 over thirteen...

This paper explores a number of simple and effective techniques to adapt statistical machine translation (SMT) systems in the medical domain. Comparative experiments are conducted on large corpora for six language pairs. We not only compare each adapted system with the baseline, but also combine them to further improve the domain-specific systems. Finally, we attend the WMT2014 medical summary ...

In this paper, we propose a framework for combining outputs from multiple on-line machine translation systems. This framework consists of several modules, including selection, substitution, insertion, and deletion. We evaluate the combination framework on IWSLT07 in travel domain, for the translation direction from Chinese to English. Three different on-line machine translation systems, Google,...

Based upon a statistically trained speech translation system, in this study, we try to combine distinctive features derived from the two modules: speech recognition and statistical machine translation, in a loglinear model. The translation hypotheses are then rescored and translation performance is improved. The standard translation evaluation metrics, including BLEU, NIST, multiple reference w...

In this paper, we describe the techniques that are explored in the AppTek system to enhance the translations in the Turkish to English track of IWSLT09. The submission was generated using a phrase-based statistical machine translation system. We also researched the usage of morpho-syntactic information and the application of word reordering in order to improve the translation results. The resul...

Instance weighting has been widely applied to phrase-based machine translation domain adaptation. However, it is challenging to be applied to Neural Machine Translation (NMT) directly, because NMT is not a linear model. In this paper, two instance weighting technologies, i.e., sentence weighting and domain weighting with a dynamic weight learning strategy, are proposed for NMT domain adaptation...