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The TALP-UPC Approach to System Selection: Asiya Features and Pairwise Classification Using Random Forests.

, , , , and . WMT@ACL, page 359-364. The Association for Computer Linguistics, (2013)

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Efficient Interactive Weight Tuning For Tts Synthesis: Reducing User Fatigue By Improving User Consistency., , , , and . ICASSP (1), page 865-868. IEEE, (2006)Dealing with Input Noise in Statistical Machine Translation., and . COLING (Posters), page 319-328. Indian Institute of Technology Bombay, (2012)Leveraging Online User Feedback to Improve Statistical Machine Translation., , , , and . J. Artif. Intell. Res., (2015)Perception-guided and phonetic clustering weight tuning based on diphone pairs for unit selection TTS., , , , , and . INTERSPEECH, page 1221-1224. ISCA, (2004)GTM User Modeling for aIGA Weight Tuning in TTS Synthesis., and . Encyclopedia of Artificial Intelligence, IGI Global, (2009)Efficient and reliable perceptual weight tuning for unit-selection text-to-speech synthesis based on active interactive genetic algorithms: A proof-of-concept., , and . Speech Commun., 53 (5): 786-800 (2011)Improving English to Spanish Out-of-Domain Translations by Morphology Generalization and Generation., , , and . MonoMT@AMTA, Association for Machine Translation in the Americas, (2012)A Graph-based Strategy to Streamline Translation Quality Assessments., , and . AMTA, Association for Machine Translation in the Americas, (2020)The TALP-UPC Phrase-Based Translation Systems for WMT13: System Combination with Morphology Generation, Domain Adaptation and Corpus Filtering., , , , , and . WMT@ACL, page 134-140. The Association for Computer Linguistics, (2013)Real-life Translation Quality Estimation for MT System Selection., , and . MTSummit, (2013)