LIMSI Submission for WMT’14 QE Task
By Guillaume Wisniewski, Nicolas Pécheux, Alexander Allauzen, François Yvon. 2014
14 citations recorded by the source corpus.
This paper describes LIMSI participation to the WMT’14 Shared Task on Quality Estimation; we took part to the wordlevel quality estimation task for English to Spanish translations. Our system relies on a random forest classifier, an ensemble method that has been shown to be very competitive for this kind of task, when only a few dense and continuous features are used. Notably, only 16 features are used in our experiments. These features describe, on the one hand, the quality of the association between the source sentence and each target word and, on the other hand, the fluency of the hypothesis. Since the evaluation criterion is the f1 measure, a specific tuning strategy is proposed to select the optimal values for the hyper-parameters. Overall, our system achieves a 0.67 f1 score on a randomly extracted test set.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lucia Specia
- Ondrej Bojar
- Philipp Koehn
- Graham Neubig
- Matt Post
- Barry Haddow
- Rico Sennrich
- Christof Monz
In Natural Language Processing
- Lucia Specia
- Philipp Koehn
- Ondrej Bojar
- Barry Haddow
- Matt Post
- Christof Monz
- Rico Sennrich
- Graham Neubig
Citations in the corpus
- Findings of the 2014 Workshop on Statistical Machine Translation (2014, background): “, 2014) LIMSI Laboratoire d’Informatique pour la Mécanique et les Sciences de l’Ingénieur, France (Wisniewski et al., 2014) MULTILIZER Multilizer, Finland RTM-DCU Dublin City University Team 2, Ireland (Bicici and Way, 2014) SHEF-lite University of Sheffield Team 1, UK (Beck et al.”
- 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.”
- LORIA System for the WMT15 Quality Estimation Shared Task (2015, methodology): “(Luong et al., 2014; Wisniewski et al., 2014) works are applied to word-level Quality Estimation while we deal with sentence-level Quality Estimation.”