LIMSI’s Contribution to the WMT’16 Biomedical Translation Task
By Julia Ive, Aurélien Max, François Yvon. 2016
5 citations recorded by the source corpus.
The article describes LIMSI’s submission to the first WMT’16 shared biomedical translation task, focusing on the sole English-French translation direction. Our main submission is the output of a MOSES-based statistical machine translation (SMT) system, rescored with Structured OUtput Layer (SOUL) neural network models. We also present an attempt to circumvent syntactic complexity: our proposal combines the outputs of PBSMT systems trained either to translate entire source sentences or specific syntactic constructs extracted from those sentences. The approach is implemented using Confusion Network (CN) decoding. The quality of the combined output is comparable to the quality of our main system.
Subfields: Natural Language Processing
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In Natural Language Processing
- Ondrej Bojar
- Philipp Koehn
- Lucia Specia
- Barry Haddow
- Christof Monz
- Yvette Graham
- Matt Post
- Graham Neubig
Citations in the corpus
- Findings of the 2016 Conference on Machine Translation (2016, background): “, 2016) IXA University of the Basque Country UPV/EHU, Spain (Perez-de Viñaspre and Labaka, 2016) LIMSI-TLP LIMSI, France (Ive et al., 2016) TALP-UPC Universitat Politècnica de Catalunya, Spain (Costa-jussà et al.”