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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach ...
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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
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Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation ...
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Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation
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The Universitat d'Alacant submissions to the English-to-Kazakh news translation task at WMT 2019 ...
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The Universitat d'Alacant submissions to the English-to-Kazakh news translation task at WMT 2019 ...
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Reading comprehension of machine translation output: what makes for a better read?
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In: Castilho, Sheila orcid:0000-0002-8416-6555 and Guerberof Arenas, Ana orcid:0000-0001-9820-7074 (2018) Reading comprehension of machine translation output: what makes for a better read? In: 21st Annual Conference of the European for Machine Translation, 28-30 May 2018, Alacant/Alicante, Spain. ISBN 978-84-09-01901-4 (2018)
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Comparative Human and Automatic Evaluation of Glass-Box and Black-Box Approaches to Interactive Translation Prediction
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Assisting non-expert speakers of under-resourced languages in assigning stems and inflectional paradigms to new word entries of morphological dictionaries
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Comparative Human and Automatic Evaluation of Glass-Box and Black-Box Approaches to Interactive Translation Prediction
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 97-108 (2017) (2017)
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Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation
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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).
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Keyword:
Integrating; Lenguajes y Sistemas Informáticos; Linguistic resources; Machine translation; Phrase-based; Rule-based; Shallow-transfer
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URL: https://doi.org/10.1613/jair.4761
http://hdl.handle.net/10045/52507
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RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation
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RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation
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In: Prague Bulletin of Mathematical Linguistics , Vol 106, Iss 1, Pp 193-204 (2016) (2016)
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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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An Open-Source Web-Based Tool for Resource-Agnostic Interactive Translation Prediction
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An open-source toolkit for integrating shallow-transfer rules into phrase-based statistical machine translation
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Choosing the correct paradigm for unknown words in rule-based machine translation systems
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