Target-Centric Features for Translation Quality Estimation
By Chris Hokamp, Iacer Calixto, Joachim Wagner, Jian Zhang. 2014
5 citations recorded by the source corpus.
We describe the DCU-MIXED and DCUSVR submissions to the WMT-14 Quality Estimation task 1.1, predicting sentencelevel perceived post-editing effort. Feature design focuses on target-side features as we hypothesise that the source side has little effect on the quality of human translations, which are included in task 1.1 of this year’s WMT Quality Estimation shared task. We experiment with features of the QuEst framework, features of our past work, and three novel feature sets. Despite these efforts, our two systems perform poorly in the competition. Follow up experiments indicate that the poor performance is due to improperly optimised parameters.
Subfields: Artificial Intelligence, Natural Language Processing
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In Artificial Intelligence
- Philipp Koehn
- Ondrej Bojar
- Lucia Specia
- Graham Neubig
- Matt Post
- Barry Haddow
- Rico Sennrich
- Christof Monz
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, background): “ID Participating team DCU Dublin City University Team 1, Ireland (Hokamp et al., 2014) The sentence-level QE submissions (T1.”