Postech’s System Description for Medical Text Translation Task
By Jianri Li, Se-Jong Kim, Hwidong Na, Jong-Hyeok Lee. 2014
4 citations recorded by the source corpus.
This short paper presents a system description for intrinsic evaluation of the WMT 14’s medical text translation task. Our systems consist of phrase-based statistical machine translation system and query translation system between German-English language pairs. Our work focuses on the query translation task and we achieved the highest BLEU score among the all submitted systems for the English-German intrinsic query translation evaluation.
Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing
How researchers see this paper
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In Artificial Intelligence
- Philipp Koehn
- Ondrej Bojar
- Lucia Specia
- Graham Neubig
- Matt Post
- Barry Haddow
- Rico Sennrich
- Christof Monz
In Molecular Biology
- Lucia Specia
- Ondrej Bojar
- Philipp Koehn
- Barry Haddow
- Christof Monz
- Matt Post
- Yvette Graham
- Varvara Logacheva
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) POSTECH Pohang University of Science and Technology (Li et al., 2014a) UEDIN University of Edinburgh (Durrani et al.”