When Part-of-Speech annotated data is scarce, e.g. for under-resourced languages, one can turn to cross-lingual transfer and crawled dictionaries to collect partially supervised data. We cast this problem in the framework of ambiguous learning and show how to learn an accurate history-based model. Experiments on ten languages show significant improvements over prior state of the art performance.

This paper describes the two systems submitted by LIMSI to the WMT'15 Shared Task on Automatic Post-Editing. The first one relies on a reformulation of the APE task as a Machine Translation task; the second implements a simple rule-based approach. Neither of these two systems manage to improve the automatic translation. We show, by carefully analyzing the failure of our systems that this counterperformance mainly results from the inconsistency in the annotations.

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).

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