Referential Translation Machines for Quality Estimation

By Ergun Biçici. 2013

44 citations recorded by the source corpus.

Subfields: Evaluation & Benchmarks, Machine Translation

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Citations in the corpus

Using on-line available sources of bilingual information for word-level machine translation quality estimation (2015, methodology): “Bic¸ici (2013) proposes a strategy for extending this kind of system-dependent features to what could be called a system-independent scenario.”
Pushing the Limits of Translation Quality Estimation (2017, background): “Past approaches to this problem include linear classiﬁers with handcrafted features (Uefﬁng and Ney, 2007; Bic¸ici, 2013; Shah et al., 2013; Luong et al., 2014), sometimes subject to feature selection (Avramidis, 2012; Beck et al., 2013), recurrent neural networks (de Souza et al., 2014; Kim and…”
Using on-line available sources of bilingual information for word-level machine translation quality estimation (2015, background): “Biçici (2013) proposes a strategy for extending this kind of system-dependent features to what could be called a system-independent scenario.”
Referential Translation Machines for Predicting Translation Quality (2014, methodology): “We extend the MTPP model we used last year (Biçici, 2013) in its learning module and the features included.”
Quality estimation for translation selection (2014, methodology): “Bic¸ici [2013] uses language and MT system independent features to predict F1 scores with regression algorithms.”
Referential Translation Machines for Predicting Translation Quality and Related Statistics (2015, methodology): “We use mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), and correlation ( r ) as well as relative MAE (MAER) and relative RAE (MRAER) to evaluate (Bic¸ici, 2015; Bic¸ici, 2013).”
Referential translation machines for predicting semantic similarity (2015, methodology): “We use ParFDA instance selection model, which is developed for fast deployment of accurate SMT systems (Bic¸ici et al. 2015a; Bic¸ici and Yuret 2015), for selecting the interpretants, and build an MTPPS model.”
LIG System for Word Level QE task at WMT14 (2014, background): “Meanwhile, Bicici (2013) extended the global learning model by dynamic training with”
Referential Translation Machines for Predicting Translation Performance (2016, methodology): “In Table 7, we list the RTM test results for tasks and subtasks that predict HTER or METEOR from QET16, QET15 (Bic¸ici et al., 2015b), QET14 (Bic¸ici and Way, 2014), and QET13 (Bic¸ici, 2013”
Findings of the 2013 Workshop on Statistical Machine Translation (2013, background): “3, T2): CNGL systems are based on referential translation machines (RTM) (Bic¸ici and van Genabith, 2013), parallel feature decay algorithms (FDA) (Bicici, 2013a), and machine translation performance predictor (MTPP) (Bic¸ici et al., 2013), all of which allow to obtain language and MT system-indepen”
Target-Centric Features for Translation Quality Estimation (2014, methodology): “Finally, we would like to integrate referential translation machines (Bic¸ici, 2013; Bic¸ici and Way, 2014) into our system as they performed well in the WMT quality estimation tasks this and last year.”