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1
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
Sánchez-Cartagena, Víctor M.; Sánchez-Martínez, Felipe; Pérez-Ortiz, Juan Antonio. - : Association for Computational Linguistics, 2021
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2
Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation
Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio; Sánchez-Martínez, Felipe. - : Association for Computational Linguistics, 2020
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3
Assisting non-expert speakers of under-resourced languages in assigning stems and inflectional paradigms to new word entries of morphological dictionaries
Forcada, Mikel L.; Carrasco, Rafael C.; Pérez-Ortiz, Juan Antonio. - : Springer Science+Business Media Dordrecht, 2017
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4
Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation
Abstract: We describe a hybridisation strategy whose objective is to integrate linguistic resources from shallow-transfer rule-based machine translation (RBMT) into phrase-based statistical machine translation (PBSMT). It basically consists of enriching the phrase table of a PBSMT system with bilingual phrase pairs matching transfer rules and dictionary entries from a shallow-transfer RBMT system. This new strategy takes advantage of how the linguistic resources are used by the RBMT system to segment the source-language sentences to be translated, and overcomes the limitations of existing hybrid approaches that treat the RBMT systems as a black box. Experimental results confirm that our approach delivers translations of higher quality than existing ones, and that it is specially useful when the parallel corpus available for training the SMT system is small or when translating out-of-domain texts that are well covered by the RBMT dictionaries. A combination of this approach with a recently proposed unsupervised shallow-transfer rule inference algorithm results in a significantly greater translation quality than that of a baseline PBSMT; in this case, the only hand-crafted resource used are the dictionaries commonly used in RBMT. Moreover, the translation quality achieved by the hybrid system built with automatically inferred rules is similar to that obtained by those built with hand-crafted rules. ; Research funded by the Spanish Ministry of Economy and Competitiveness through projects TIN2009-14009-C02-01 and TIN2012-32615, by Generalitat Valenciana through grant ACIF 2010/174, and by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran).
Keyword: Integrating; Lenguajes y Sistemas Informáticos; Linguistic resources; Machine translation; Phrase-based; Rule-based; Shallow-transfer
URL: https://doi.org/10.1613/jair.4761
http://hdl.handle.net/10045/52507
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5
RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation
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6
A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora
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7
An open-source toolkit for integrating shallow-transfer rules into phrase-based statistical machine translation
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8
Choosing the correct paradigm for unknown words in rule-based machine translation systems
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9
The Universitat d’Alacant hybrid machine translation system for WMT 2011
Sánchez-Cartagena, Víctor M.; Sánchez-Martínez, Felipe; Pérez-Ortiz, Juan Antonio. - : Association for Computational Linguistics, 2011
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10
Integrating shallow-transfer rules into phrase-based statistical machine translation
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11
Enriching a statistical machine translation system trained on small parallel corpora with rule-based bilingual phrases
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12
ScaleMT: a free/open-source framework for building scalable machine translation web services
Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio. - : Charles University in Prague. Institute of Formal and Applied Linguistics, 2009. : Versita, 2009
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13
Tradubi: open-source social translation for the Apertium machine translation platform
Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio. - : Charles University in Prague. Institute of Formal and Applied Linguistics, 2009. : Versita, 2009
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