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.
Cite
CITATION STYLE
Ive, J., Max, A., & Yvon, F. (2016). LIMSI’s Contribution to the WMT’16 Biomedical Translation Task. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 2, pp. 469–476). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-2337
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