LIMSI @ WMT’14 Medical Translation Task

By Nicolas Pécheux, Li Gong, Quoc-Khanh Do, Benjamin Marie, Yulia Ivanishcheva, Alexander Allauzen, Thomas Lavergne, Jan Niehues, Aurélien Max, François Yvon. 2014

11 citations recorded by the source corpus.

This paper describes LIMSI’s submission to the first medical translation task at WMT’14. We report results for EnglishFrench on the subtask of sentence translation from summaries of medical articles. Our main submission uses a combination of NCODE (n-gram-based) and MOSES (phrase-based) output and continuous-space language models used in a post-processing step for each system. Other characteristics of our submission include: the use of sampling for building MOSES’ phrase table; the implementation of the vector space model proposed by Chen et al. (2013); adaptation of the POStagger used by NCODE to the medical domain; and a report of error analysis based on the typology of Vilar et al. (2006).

Subfields: Artificial Intelligence, Machine Translation, Medical & Health AI

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. François Yvon
  2. Alexander Allauzen
  3. Elena Knyazeva
  4. Aurélien Max
  5. Guillaume Wisniewski
  6. Nicolas Pécheux
  7. Ophélie Lacroix
  8. Quoc-Khanh Do

In Machine Translation

  1. Quoc-Khanh Do
  2. François Yvon
  3. Alexander Allauzen
  4. Franck Burlot
  5. Yonatan Belinkov
  6. Holger Schwenk
  7. James R. Glass
  8. Andreas Vlachos

Citations in the corpus