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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 GPLSI team at CheckThat! 2021: Fine-tuning BETO and RoBERTa Sepúlveda-Torres, Robiert; Saquete Boró, Estela. - : CEUR, 2021 BASE Show details 2 Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio; Sánchez-Martínez, Felipe. - : AI Access Foundation, 2016 BASE Show details 3 RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation Pérez-Ortiz, Juan Antonio; Sánchez-Cartagena, Víctor M.; Sánchez-Martínez, Felipe. - : De Gruyter, 2016 BASE Show details 4 A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio; Sánchez-Martínez, Felipe. - : Elsevier, 2015 Abstract: Statistical and rule-based methods are complementary approaches to machine translation (MT) that have different strengths and weaknesses. This complementarity has, over the last few years, resulted in the consolidation of a growing interest in hybrid systems that combine both data-driven and linguistic approaches. In this paper, we address the situation in which the amount of bilingual resources that is available for a particular language pair is not sufficiently large to train a competitive statistical MT system, but the cost and slow development cycles of rule-based MT systems cannot be afforded either. In this context, we formalise a new method that uses scarce parallel corpora to automatically infer a set of shallow-transfer rules to be integrated into a rule-based MT system, thus avoiding the need for human experts to handcraft these rules. Our work is based on the alignment template approach to phrase-based statistical MT, but the definition of the alignment template is extended to encompass different generalisation levels. It is also greatly inspired by the work of Sánchez-Martínez and Forcada (2009) in which alignment templates were also considered for shallow-transfer rule inference. However, our approach overcomes many relevant limitations of that work, principally those related to the inability to find the correct generalisation level for the alignment templates, and to select the subset of alignment templates that ensures an adequate segmentation of the input sentences by the rules eventually obtained. Unlike previous approaches in literature, our formalism does not require linguistic knowledge about the languages involved in the translation. Moreover, it is the first time that conflicts between rules are resolved by choosing the most appropriate ones according to a global minimisation function rather than proceeding in a pairwise greedy fashion. Experiments conducted using five different language pairs with the free/open-source rule-based MT platform Apertium show that translation quality significantly improves when compared to the method proposed by Sánchez-Martínez and Forcada (2009), and is close to that obtained using handcrafted rules. For some language pairs, our approach is even able to outperform them. Moreover, the resulting number of rules is considerably smaller, which eases human revision and maintenance. ; Research funded by Universitat d’Alacant through project GRE11-20, 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: Hybrid machine translation; Lenguajes y Sistemas Informáticos; Machine translation; Transfer rule inference URL: https://doi.org/10.1016/j.csl.2014.10.003 http://hdl.handle.net/10045/52497 BASE Hide details 5 An open-source toolkit for integrating shallow-transfer rules into phrase-based statistical machine translation Sánchez-Cartagena, Víctor M.; Sánchez-Martínez, Felipe; Pérez-Ortiz, Juan Antonio. - : CSLI Publications, 2012 BASE Show details 6 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 BASE Show details 7 Integrating shallow-transfer rules into phrase-based statistical machine translation Sánchez-Cartagena, Víctor M.; Sánchez-Martínez, Felipe; Pérez-Ortiz, Juan Antonio. - : Machine Translation Summit, 2011 BASE Show details 8 Inferring shallow-transfer machine translation rules from small parallel corpora Sánchez-Martínez, Felipe; Forcada, Mikel L.. - : AI Access Foundation, 2008 BASE Show details 9 An open-source shallow-transfer machine translation toolbox: consequences of its release and availability Armentano Oller, Carme; Corbí Bellot, Antonio Miguel; Forcada, Mikel L.. - : OSMaTran, 2005 BASE Show details 10 An open-source shallow-transfer machine translation engine for the Romance languages of Spain Corbí Bellot, Antonio Miguel; Forcada, Mikel L.; Ortiz Rojas, Sergio. - : European Association for Machine Translation, 2005 BASE Show details

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