LIG System for WMT13 QE Task: Investigating the Usefulness of Features in Word Confidence Estimation for MT
By Ngoc-Quang Luong, Benjamin Lecouteux, Laurent Besacier. 2013
26 citations recorded by the source corpus.
Subfields: Natural Language Processing
How researchers see this paper
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In Natural Language Processing
- Philipp Koehn
- Lucia Specia
- Ondrej Bojar
- Barry Haddow
- Matt Post
- Christof Monz
- Matteo Negri
- Varvara Logacheva
Citations in the corpus
- Findings of the 2015 Workshop on Statistical Machine Translation (2015, background): “Luong et al. (2013) had previously reported that pseudo-reference features improve the accuracy of word-level predictions.”
- USFD’s Phrase-level Quality Estimation Systems (2016, methodology): “We chose to train a Conditional Random Fields (CRF) model because it has shown high performance in wordlevel QE (Luong et al., 2013) as well as phraselevel QE (Logacheva and Specia, 2015) tasks.”
- Findings of the 2013 Workshop on Statistical Machine Translation (2013, background): “, 2013) LIG Laboratoire d’Informatique Grenoble, France (Luong et al., 2013) LIMSI Laboratoire d’Informatique pour la Mécanique et les Sciences de l’Ingénieur, France (Singh et al.”