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EnglishMachine Translation (MT) Quality Estimation (QE) aims to automatically measure the quality of MT system output without reference translations. In spite of the progress achieved in re- cent years, current MT QE systems are not capable of dealing with data coming from different train/test distributions or domains, and scenarios in which training data is scarce. We investigate different multitask learning methods that can cope with such limitations and show that they over- come current state-of-the-art methods in real-world conditions where training and test data come from different domains.
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| Titolo: | Machine Translation Quality Estimation Across Domains | |
| Autori: | ||
| Data di pubblicazione: | 2014 | |
| Abstract: | Machine Translation (MT) Quality Estimation (QE) aims to automatically measure the quality of MT system output without reference translations. In spite of the progress achieved in re- cent years, current MT QE systems are not capable of dealing with data coming from different train/test distributions or domains, and scenarios in which training data is scarce. We investigate different multitask learning methods that can cope with such limitations and show that they over- come current state-of-the-art methods in real-world conditions where training and test data come from different domains. | |
| Handle: | http://hdl.handle.net/11582/250637 | |
| ISBN: | 9781941643266 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11582/250637
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