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not found correspond to those descriptions of greater
word length. In some cases, this may be due to the
fact that longer medical descriptions generally have a
sentence structure with bare verbs and a telegraphic
style. This type of constructions is not very com-
mon neither in the training corpus nor in the corpus
of scientific texts. Also, additional human translators
should translate the two test sets and a proper mea-
surement for human inter-translator agreement should
be obtained.
5 CONCLUSIONS
We explored the use of alternative tools to assist the
translation of medical terminology from Spanish to
Portuguese. General purpose SMTs showed a num-
ber of deficiencies which limited their use for this
purpose. We implemented an M-SMT for Spanish-
Portuguese translation which showed much better
performance than general purpose ones. The work
described here showed that an approach based on us-
ing parallel corpora and linguistic mappings to reduce
out of vocabulary words have been successful to ad-
dress the problem with very good performance. In
future work, we will consider the use of other tools
and techniques to improve the results of a SMT for
the medical domain.
REFERENCES
Armentano-Oller, C., Carrasco, R. C., Corb
´
ı-Bellot, A. M.,
Forcada, M. L., Ginest
´
ı-Rosell, M., Ortiz-Rojas, S.,
P
´
erez-Ortiz, J. A., Ram
´
ırez-S
´
anchez, G., S
´
anchez-
Mart
´
ınez, F., and Scalco, M. A. (2006). Open-source
portuguese–spanish machine translation. In Interna-
tional Workshop on Computational Processing of the
Portuguese Language, pages 50–59. Springer.
Aziz, W. and Specia, L. (2011). Fully automatic compi-
lation of portuguese-english and portuguese-spanish
parallel corpora.
Barcelos Junior, C. L., Andrade, R., Ribeiro, L. A., and von
Wangenheim, A. (2008). Busca sem
ˆ
antica aplicada a
informac¸
˜
oes cl
´
ınicas. In XI Congresso Brasileiro de
Inform
´
atica em Sa
´
ude.
Bojar, O., Chatterjee, R., Federmann, C., Graham, Y., Had-
dow, B., Huck, M., Jimeno Yepes, A., Koehn, P., Lo-
gacheva, V., Monz, C., Negri, M., Neveol, A., Neves,
M., Popel, M., Post, M., Rubino, R., Scarton, C., Spe-
cia, L., Turchi, M., Verspoor, K., and Zampieri, M.
(2016). Findings of the 2016 conference on machine
translation. In Proceedings of the First Conference
on Machine Translation, pages 131–198, Berlin, Ger-
many. Association for Computational Linguistics.
Bowker, L. and Fisher, D. (2010). Computer-aided transla-
tion. Handbook of translation studies, 1:60–65.
Clark, J. H., Dyer, C., Lavie, A., and Smith, N. A. (2011).
Better hypothesis testing for statistical machine trans-
lation: Controlling for optimizer instability. In Pro-
ceedings of the 49th Annual Meeting of the Associa-
tion for Computational Linguistics: Human Language
Technologies: short papers-Volume 2, pages 176–181.
Association for Computational Linguistics.
Costa-juss
`
a, M., Farr
´
us, M., and Serrano-Pons, J. (2012).
Machine translation in medicine. a quality analysis
of statistical machine translation in the medical do-
main. Proceedings of the 1st Virtual International
Conference on Advanced Research in Scientific Areas
(ARSA-2012).
Denkowski, M. and Lavie, A. (2014). Meteor universal:
Language specific translation evaluation for any target
language. In Proceedings of the EACL 2014 Workshop
on Statistical Machine Translation.
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y.,
Chen, Z., Thorat, N., Vi
´
egas, F., Wattenberg, M., Cor-
rado, G., et al. (2016). Google’s multilingual neural
machine translation system: enabling zero-shot trans-
lation. arXiv preprint arXiv:1611.04558.
Koehn, P. and Haddow, B. (2009). Interactive assistance to
human translators using statistical machine translation
methods. MT Summit XII.
Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Fed-
erico, M., Bertoldi, N., Cowan, B., Shen, W., Moran,
C., Zens, R., et al. (2007). Moses: Open source toolkit
for statistical machine translation. In Proceedings of
the 45th annual meeting of the ACL on interactive
poster and demonstration sessions, pages 177–180.
Association for Computational Linguistics.
Koehn, P. and Knight, K. (2003). Feature-rich statistical
translation of noun phrases. In Proceedings of the 41st
Annual Meeting of the Association for Computational
Linguistics, 7-12 July 2003, Sapporo Convention Cen-
ter, Sapporo, Japan., pages 311–318.
Lucas Emanuel Silva e Oliveira, Sadid A. Hasan, O. F. e.
C. M. C. M. (2016). Translation of umls ontologies
from european portuguese to brazilian portuguese. In
XV Congresso Brasileiro de Inform
´
atica em Sa
´
ude 27
a 30 de novembro - Goi
ˆ
ania - Brasil.
Masselot, F. and Ribiczey, P. (2010). Using the apertium
spanish-brazilian portuguese machine translation sys-
tem for localization.
Nyberg, E., Mitamura, T., and Huijsen, W.-O. (2003). Con-
trolled language for authoring and translation. Com-
puters and Translation: A Translator’s Guide, 35:245.
Och, F. J. and Ney, H. (2003). A systematic comparison of
various statistical alignment models. Computational
Linguistics, 29(1):19–51.
Pacheco, E. J. (2009). Morphomap: mapeamento au-
tom
´
atico de narrativas cl
´
ınicas para uma terminologia
m
´
edica.
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002).
Bleu: a method for automatic evaluation of machine
translation. In Proceedings of the 40th annual meeting
on association for computational linguistics, pages
311–318. Association for Computational Linguistics.
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