Unbabel’s Participation in the WMT16 Word-Level Translation Quality Estimation Shared Task

By André F. T. Martins, Ramón Fernandez Astudillo, Chris Hokamp, Fabio Kepler. 2016

41 citations recorded by the source corpus.

This paper presents the contribution of the Unbabel team to the WMT 2016 Shared Task on Word-Level Translation Quality Estimation. We describe our two submitted systems: (i) UNBABEL - LINEAR , a feature-rich sequential linear model with syntactic features, and (ii) UNBABEL - ENSEMBLE , a stacked combination of the linear system with three different deep neural networks, mixing feed-forward, convolutional, and recurrent layers. Our systems achieved F OK 1 × F BAD 1 scores of 46.29% and 49.52%, respectively, which were the two highest scores in the challenge.

Subfields: Artificial Intelligence, Evaluation & Benchmarks

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Citations in the corpus

Findings of the 2016 Conference on Machine Translation (2016, background): “, 2016) UNBABEL Unbabel, Portugal (Martins et al., 2016) USFD University of Sheffield, UK (Logacheva et al.”
Findings of the 2017 Conference on Machine Translation (WMT17) (2017, background): “In addition to that, 6 new features were included which contain combinations of other features, and which proved useful in (Kreutzer et al., 2015; Martins et al., 2016): • Target word + left context.”
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation (2017, methodology): “Previous work has shown that alignment information between source and target is a critical component of current state-of-the-art word level QE systems (Kreutzer et al., 2015; Martins et al., 2016).”
Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation (2017, methodology): “Martins et al. (2016) combined 15 instances of neural models to make ensembles; they used three types of neural models and trained ﬁve instances for each type by using different data shufﬂes.”
Learning What’s Easy: Fully Differentiable Neural Easy-First Taggers (2017, methodology): “The winner system, which achieved 49.52 F 1 - MULT , was considerably more complex than ours, using an ensemble of three neural networks with a linear system (Martins et al., 2016).”