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EnglishThe automatic estimation of machine translation (MT) output quality is a hard task in which the selection of the appro- priate algorithm and the most predictive features over reasonably sized training sets plays a crucial role. When moving from controlled lab evaluations to real-life sce- narios the task becomes even harder. For current MT quality estimation (QE) sys- tems, additional complexity comes from the difficulty to model user and domain changes. Indeed, the instability of the sys- tems with respect to data coming from dif- ferent distributions calls for adaptive so- lutions that react to new operating con- ditions. To tackle this issue we propose an online framework for adaptive QE that targets reactivity and robustness to user and domain changes. Contrastive exper- iments in different testing conditions in- volving user and domain changes demon- strate the effectiveness of our approach.
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| Titolo: | Adaptive Quality Estimation for Machine Translation | |
| Autori: | ||
| Data di pubblicazione: | 2014 | |
| Abstract: | The automatic estimation of machine translation (MT) output quality is a hard task in which the selection of the appro- priate algorithm and the most predictive features over reasonably sized training sets plays a crucial role. When moving from controlled lab evaluations to real-life sce- narios the task becomes even harder. For current MT quality estimation (QE) sys- tems, additional complexity comes from the difficulty to model user and domain changes. Indeed, the instability of the sys- tems with respect to data coming from dif- ferent distributions calls for adaptive so- lutions that react to new operating con- ditions. To tackle this issue we propose an online framework for adaptive QE that targets reactivity and robustness to user and domain changes. Contrastive exper- iments in different testing conditions in- volving user and domain changes demon- strate the effectiveness of our approach. | |
| Handle: | http://hdl.handle.net/11582/250641 | |
| ISBN: | 9781937284725 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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