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A mutation in the POT1 gene is responsible for cardiac angiosarcoma in TP53-negative Li-Fraumeni-like families
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Interactions between tick and transmitted pathogens evolved to minimise competition through nested and coherent networks
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SIMPLE: a Language for the Specification of Protocols, Similar to Natural Language
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A vector representation of Fluid Construction Grammar using Holographic Reduced Representations
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Loss of Tribbles pseudokinase-3 promotes Akt-driven tumorigenesis via FOXO inactivation
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Enological characterization of Spanish Saccharomyces kudriavzevii strains, one of the closest relatives to parental strains of winemaking and brewing Saccharomyces cerevisiae × S. kudriavzevii hybrids
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Upward Refinement for Conceptual Blending in Description Logic — An ASP-based Approach and Case Study in EL++—
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Common genetic variants influence human subcortical brain structures.
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In: ISSN: 0028-0836 ; EISSN: 1476-4679 ; Nature ; https://hal.archives-ouvertes.fr/hal-01196805 ; Nature, Nature Publishing Group, 2015, 520 (7546), pp.224-9. ⟨10.1038/nature14101⟩ (2015)
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Exploring Spanish Health Social Media for detecting drug effects
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Random forest-based prediction of Parkinson's disease progression using acoustic, ASR and intelligibility features
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Depression Awareness and Self-Management Through the Internet: Protocol for an Internationally Standardized Approach
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Applying basic features from sentiment analysis on automatic irony detection
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Translating without In-domain Corpus: Machine TranslationPost-Editing with Online Learning Techniques
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Abstract:
[EN] Globalization has dramatically increased the need of translating information from one language to another. Frequently, such translation needs should be satisfied under very tight time constraints. Machine translation (MT) techniques can constitute a solution to this overly complex problem. However, the documents to be translated in real scenarios are often limited to a specific domain, such as a particular type of medical or legal text. This situation seriously hinders the applicability of MT, since it is usually expensive to build a reliable translation system, no matter what technology is used, due to the linguistic resources that are required to build them, such as dictionaries, translation memories or parallel texts. In order to solve this problem, we propose the application of automatic post-editing in an online learning framework. Our proposed technique allows the human expert to translate in a specific domain by using a base translation system designed to work in a general domain whose output is corrected (or adapted to the specific domain) by means of an automatic post-editing module. This automatic post-editing module learns to make its corrections from user feedback in real time by means of online learning techniques. We have validated our system using different translation technologies to implement the base translation system, as well as several texts involving different domains and languages. In most cases, our results show significant improvements in terms of BLEU (up to 16 points) with respect to the baseline systems. The proposed technique works effectively when the n-grams of the document to be translated presents a certain rate of repetition, situation which is common according to the document-internal repetition property. ; Work partially supported by the European Union 7th Framework Programme (FP7/2007-2013) under the CasMaCat Project (Grant Agreement No. 287576), by Spanish MICINN under Grant TIN2012-31723, and by the Generalitat Valenciana under Grant ALMPR ALMAMATER (PROMETEUII/2014/030) and under Grant IMASI (ISIC/2012/004). ; Lagarda Arroyo, AL.; Ortiz Martínez, D.; Alabau, V.; Casacuberta Nolla, F. (2015). Translating without In-domain Corpus: Machine TranslationPost-Editing with Online Learning Techniques. Computer Speech and Language. 32(1):109-134. https://doi.org/10.1016/j.csl.2014.10.004 ; S ; 109 ; 134 ; 32 ; 1
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Keyword:
Automatic post-editing; Interactive machine translation; Machine translation; Online learning; Statistical machine translation
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URL: https://doi.org/10.1016/j.csl.2014.10.004 http://hdl.handle.net/10251/63702
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An Empirical Analysis of Data Selection Techniques in Statistical Machine Translation ; Análisis empírico de técnicas de selección de datos en traducción automática estadística
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Developing CALL for heritage languages: The 7 Keys of the Dragon
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Contex-aware gestures for mixed-initiative text editings UIs
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