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Artif Intell Med. 2014 Jul;61(3):165-85. doi: 10.1016/j.artmed.2014.01.004. Epub 2014 Feb 5.

Adaptation of machine translation for multilingual information retrieval in the medical domain.

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Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University in Prague, Malostranské nám. 25, 118 00 Prague 1, Czech Republic. Electronic address:
Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University in Prague, Malostranské nám. 25, 118 00 Prague 1, Czech Republic.
CNGL Centre for Global Intelligent Content, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland.



We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve effectiveness of cross-lingual IR.


Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech-English, German-English, and French-English. MT quality is evaluated on data sets created within the Khresmoi project and IR effectiveness is tested on the CLEF eHealth 2013 data sets.


The search query translation results achieved in our experiments are outstanding - our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech-English, from 23.03 to 40.82 for German-English, and from 32.67 to 40.82 for French-English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French-English. For Czech-English and German-English, the increased MT quality does not lead to better IR results.


Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance - better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions.


Compound splitting; Cross-language information retrieval; Domain adaptation of statistical machine translation; Intelligent training data selection for machine translation; Medical query translation; Statistical machine translation

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