2013
DOI: 10.2478/pralin-2013-0008
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QuEst – Design, Implementation and Extensions of a Framework for Machine Translation Quality Estimation

Abstract: In this paper we present QE, an open source framework for machine translation quality estimation. The framework includes a feature extraction component and a machine learning component. We describe the architecture of the system and its use, focusing on the feature extraction component and on how to add new feature extractors. We also include experiments with features and learning algorithms available in the framework using the dataset of the WMT13 Quality Estimation shared task.

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Cited by 19 publications
(34 citation statements)
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“…RTM PPP models are built for each task emerging from training set, test set, and label set obtained from a parser. RTMs achieve top results in machine translation performance prediction (MTPP) in quality estimation task (Biçici et al, 2015b;Biçici, 2016), can achieve better results than open-source MTPP tool QuEst (Shah et al, 2013;Biçici and Specia, 2015), and can achieve top results in semantic similarity prediction tasks . We provide a current picture on PPP detailing prediction performance, top selected features, and lower bound on prediction error of PPP.…”
Section: Predicting Parsing Performance With Referential Translation mentioning
confidence: 99%
“…RTM PPP models are built for each task emerging from training set, test set, and label set obtained from a parser. RTMs achieve top results in machine translation performance prediction (MTPP) in quality estimation task (Biçici et al, 2015b;Biçici, 2016), can achieve better results than open-source MTPP tool QuEst (Shah et al, 2013;Biçici and Specia, 2015), and can achieve top results in semantic similarity prediction tasks . We provide a current picture on PPP detailing prediction performance, top selected features, and lower bound on prediction error of PPP.…”
Section: Predicting Parsing Performance With Referential Translation mentioning
confidence: 99%
“…The list of features and the way they are extracted were identical as described in [12]. More details could be found in [12,13,14].…”
Section: Features For Quality Estimationmentioning
confidence: 99%
“…Qualitative (Avramidis, 2016) is used for the feature generation and prediction of sentence-level ranking, whereas models produced by the popular state-ofthe-art QuEST (Shah et al, 2013) can be used for continuous score prediction. The database structure allows for loading and storing other Python-based QE models, provided that their usage is clearly documented.…”
Section: Quality Estimationmentioning
confidence: 99%