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

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/21590

dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.contributor	Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.contributor	Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.contributor.author	Formiga Fanals, Lluís
dc.contributor.author	González Bermúdez, Meritxell
dc.contributor.author	Barrón-Cedeño, Alberto
dc.contributor.author	Rodríguez Fonollosa, José Adrián
dc.contributor.author	Màrquez Villodre, Lluís
dc.date	2013
dc.identifier.citation	Formiga, 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.
dc.identifier.uri	http://hdl.handle.net/2117/21590
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Ensenyament i aprenentatge
dc.subject	Àrees temàtiques de la UPC::Ensenyament i aprenentatge::Metodologies docents
dc.subject	Translators
dc.subject	Education--Experimental methods
dc.subject	Traductors (Programes d'ordinador)
dc.subject	Ensenyament -- Mètodes experimentals
dc.title	The TALP-UPC approach to system selection: ASIYA features and pairwise classification using random forests
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.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.
dc.description.abstract	Peer Reviewed

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