This paper describes LIMSI participation to the WMT'14 Shared Task on Quality Estimation; we took part to the wordlevel quality estimation task for English to Spanish translations. Our system relies on a random forest classifier, an ensemble method that has been shown to be very competitive for this kind of task, when only a few dense and continuous features are used. Notably, only 16 features are used in our experiments. These features describe, on the one hand, the quality of the association between the source sentence and each target word and, on the other hand, the fluency of the hypothesis. Since the evaluation criterion is the f 1 measure, a specific tuning strategy is proposed to select the optimal values for the hyper-parameters. Overall, our system achieves a 0.67 f 1 score on a randomly extracted test set.

This paper describes LIMSI's submission to the first medical translation task at WMT'14. We report results for EnglishFrench on the subtask of sentence translation from summaries of medical articles.Our main submission uses a combination of NCODE (n-gram-based) and MOSES (phrase-based) output and continuous-space language models used in a post-processing step for each system. Other characteristics of our submission include: the use of sampling for building MOSES' phrase table; the implementation of the vector space model proposed by Chen et al. (2013); adaptation of the POStagger used by NCODE to the medical domain; and a report of error analysis based on the typology of Vilar et al. (2006).

This paper describes the joined submission of LIMSI and KIT to the Shared Translation Task for the German-toEnglish direction. The system consists of a phrase-based translation system using a pre-reordering approach. The baseline system already includes several models like conventional language models on different word factors and a discriminative word lexicon. This system is used to generate a k-best list. In a second step, the list is reranked using SOUL language and translation models (Le et al., 2011).Originally, SOUL translation models were applied to n-gram-based translation systems that use tuples as translation units instead of phrase pairs. In this article, we describe their integration into the KIT phrase-based system. Experimental results show that their use can yield significant improvements in terms of BLEU score.