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Title: Overcoming statistical machine translation limitations: error analysis and proposed solutions for the Catalan-Spanish language pair
Author: Farrús Cabeceran, Mireia
Costa Jussà, Marta R.
Mariño, José B.
Poch Riera, Marc
Hernández Huerta, Adolfo
Henríquez, Carlos
Rodríguez Fonollosa, José A.
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Universitat Politècnica de Catalunya
Keywords: statistical machine translation
n-gram-based translation
linguistic knowledge
grammatical categories
Issue Date: 20-Feb-2011
Publisher: Language Resources and Evaluation
Citation: Farrús Cabeceran, M., Costa-Jussà, M.R., Marino, J.B., Poch, M., Hernandez, A., Henriquez, C. & Rodriguez Fonollosa, J.A. (2011). Overcoming statistical machine translation limitations: error analysis and proposed solutions for the Catalan-Spanish language pair. Language Resources and Evaluation, 45(2), 181-208. doi: 10.1007/s10579-011-9137-0
Project identifier: info:eu-repo/grantAgreement/TEC2009-14094-C04-01
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Abstract: This work aims to improve an N-gram-based statistical machine translation system between the Catalan and Spanish languages, trained with an aligned Spanish-Catalan parallel corpus consisting of 1.7 million sentences taken from El Periódico newspaper. Starting from a linguistic error analysis above this baseline system, orthographic, morphological, lexical, semantic and syntactic problems are approached using a set of techniques. The proposed solutions include the development and application of additional statistical techniques, text pre- and post-processing tasks, and rules based on the use of grammatical categories, as well as lexical categorization. The performance of the improved system is clearly increased, as is shown in both human and automatic evaluations of the system, with a gain of about 1.1 points BLEU observed in the Spanish-to-Catalan direction of translation, and a gain of about 0.5 points in the reverse direction. The final system is freely available online as a linguistic resource.
Language: English
ISSN: 1574-020XMIAR
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