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The TALP-UPC approach to system selection: ASIYA features and pairwise classification using random forests

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Formiga Fanals, Lluís
González Bermúdez, Meritxell
Barrón-Cedeño, Alberto
Rodríguez Fonollosa, José AdriánMés informacióMés informacióMés informació
Màrquez Villodre, Lluís
Document typeConference report
Defense date2013
Rights accessRestricted access - publisher's policy
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
This paper describes the TALP-UPC participation in the WMT’13 Shared Task on Quality Estimation (QE). Our participation is reduced to task 1.2 on System Selection. We used a broad set of features (86 for German-to-English and 97 for English-to-Spanish) ranging from standard QE features to features based on pseudo-references and semantic similarity. We approached system selection by means of pairwise ranking decisions. For that, we learned Random Forest classifiers especially tailored for the problem. Evaluation at development time showed considerably good results in a cross-validation experiment, with Kendall’s values around 0.30. The results on the test set dropped significantly, raising different discussions to be taken into account.
CitationFormiga, L. [et al.]. The TALP-UPC approach to system selection: ASIYA features and pairwise classification using random forests. A: Workshop on Statistical Machine Translation. "Proceedings of the Eighth Workshop on Statistical Machine Translation". Nice: 2013, p. 359-364. 
URIhttp://hdl.handle.net/2117/21590
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