LIMSI's Contribution to the WMT'16 Biomedical Translation Task

Abstract

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.

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Cite this paper

@inproceedings{Ive2016LIMSIsCT, title={LIMSI's Contribution to the WMT'16 Biomedical Translation Task}, author={Julia Ive and Aur{\'e}lien Max and François Yvon}, booktitle={WMT}, year={2016} }