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.

Machine Translation Quality Estimation Across Domains

Turchi, Marco;Negri, Matteo
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.
9781941643266
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/250637
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