USFD’s Phrase-level Quality Estimation Systems
By Varvara Logacheva, Frédéric Blain, Lucia Specia. 2016
We describe the submissions of the University of Sheffield (USFD) for the phraselevel Quality Estimation (QE) shared task of WMT16. We test two different approaches for phrase-level QE: (i) we enrich the provided set of baseline features with information about the context of the phrases, and (ii) we exploit predictions at other granularity levels (word and sentence). These approaches perform closely in terms of multiplication of F1-scores (primary evaluation metric), but are considerably different in terms of the F1scores for individual classes.
Subfields: Evaluation & Benchmarks
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In Evaluation & Benchmarks
- Ondrej Bojar
- Lucia Specia
- Philipp Koehn
- Yvette Graham
- Matt Post
- Barry Haddow
- Christian Federmann
- Marco Turchi
Citations in the corpus
- Findings of the 2016 Conference on Machine Translation (2016, methodology): “Word-level baseline system: For Tasks 2 and 2p, the baseline features were extracted with the Marmot tool (Logacheva et al., 2016b).”