LIMSI@WMT’15 : Translation Task
By Benjamin Marie, Alexander Allauzen, Franck Burlot, Quoc-Khanh Do, Julia Ive, Elena Knyazeva, Matthieu Labeau, Thomas Lavergne, Kevin Löser, Nicolas Pécheux and 1 more. 2015
10 citations recorded by the source corpus.
This paper describes LIMSI’s submissions to the shared WMT’15 translation task. We report results for French-English, Russian-English in both directions, as well as for Finnish-into-English. Our submissions use NCODE and MOSES along with continuous space translation models in a post-processing step. The main novelties of this year’s participation are the following: for Russian-English, we investigate a tailored normalization of Russian to translate into English, and a two-step process to translate first into simplified Russian, followed by a conversion into inflected Russian. For French-English, the challenge is domain adaptation, for which only monolingual corpora are available. Finally, for the Finnish-to-English task, we explore unsupervised morphological segmentation to reduce the sparsity of data induced by the rich morphology on the Finnish side.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- François Yvon
- Alexander Allauzen
- Elena Knyazeva
- Aurélien Max
- Guillaume Wisniewski
- Ophélie Lacroix
- Lauriane Aufrant
- Franck Burlot
Citations in the corpus
- LIMSI’s Contribution to the WMT’16 Biomedical Translation Task (2016, methodology): “…made use of Structured OUtput Layer (S OUL ) neural network Language and Translation models (Le et al., 2011; Le et al., 2012a) as these have been shown to systematically improve our systems in recent evaluations (Le et al., 2012b; Al-lauzen et al., 2013; P´echeux et al., 2014; Marie et al., 2015).”
- LIMSI@WMT’16: Machine Translation of News (2016, result): “As in our previous participations (Le et al., 2012b; Allauzen et al., 2013; P´echeux et al., 2014; Marie et al., 2015), we take advantage of the proposal of (Le et al., 2012a).”