Exploring Consensus in Machine Translation for Quality Estimation
By Carolina Scarton, Lucia Specia. 2014
8 citations recorded by the source corpus.
This paper presents the use of consensus among Machine Translation (MT) systems for the WMT14 Quality Estimation shared task. Consensus is explored here by comparing the MT system output against several alternative machine translations using standard evaluation metrics. Figures extracted from such metrics are used as features to complement baseline prediction models. The hypothesis is that knowing whether the translation of interest is similar or dissimilar to translations from multiple different MT systems can provide useful information regarding the quality of such a translation.
Subfields: Natural Language Processing
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In Natural Language Processing
- Philipp Koehn
- Lucia Specia
- 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, methodology): “, 2014) USHEFF University of Sheffield Team 2, UK (Scarton and Specia, 2014) YANDEX Yandex, Russia”
- LORIA System for the WMT15 Quality Estimation Shared Task (2015, methodology): “(Scarton and Specia, 2014) use features from pseudo-reference sentences for sentence-level quality estimation.”