FBK-UPV-UEdin participation in the WMT14 Quality Estimation shared-task

By José G. C. de Souza, J. González-Rubio, Christian Buck, Marco Turchi, Matteo Negri. 2014

53 citations recorded by the source corpus.

This paper describes the joint submission of Fondazione Bruno Kessler, Universitat Politde Val` encia and University of Edinburgh to the Quality Estimation tasks of the Workshop on Statistical Machine Translation 2014. We present our submis- sions for Task 1.2, 1.3 and 2. Our systems ranked first for Task 1.2 and for the Binary and Level1 settings in Task 2.

Subfields: Artificial Intelligence, 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): “Again, one of the best results is obtained for the EN–ES data set, which would again beat the state of the art for the F 1 score for the BAD class, and Table 3: Results of the approach proposed in this paper for the same data sets used to obtain Table 2 using Reverso Context as the only source of bi”
Phrase-level Quality Estimation for Machine Translation (2015, methodology): “– FBK-UPV-UEDIN [18] — system with features from word posterior probabilities and confusion network descriptors computed over 100,000-best translations.”
Multi-level Translation Quality Prediction with QuEst++ (2015, methodology): “We explore a range of features from recent work (Bicici and Way, 2014; Camargo de Souza et al., 2014; Luong et al., 2014; Wisniewski et al., 2014), totalling 40 features of seven types: Target context These are features that explore the context of the target word.”
MARMOT: A Toolkit for Translation Quality Estimation at the Word Level (2016, methodology): “The most successful QE models use features extracted from pseudo-references (Espl`a-Gomis et al., 2015) and n-best lists (Camargo de Souza et al., 2014) produced by MT systems.”
Using on-line available sources of bilingual information for word-level machine translation quality estimation (2015, methodology): “In fact, some of the most successful approaches in the word-level MTQE task in the Workshop on Statistical Machine Translation in 2014 (WMT 2014) (Bojar et al., 2014) are based on some of the features deﬁned in that framework (Camargo de Souza et al., 2014).”