Journal Article


Zdeˇnka Urešová
Aleš Tamchyna
Rudolf Rosa
Martin Popel
Michal Novák
David Mareˇcek
Johannes Leveling
Liadh Kelly
Gareth J. F. Jones
Jaroslava Hlaváˇcová
and 4 others



natural language processing translating language information storage and retrieval algorithms software unified medical language system artificial intelligence

Adaptation of machine translation for multilingual information retrieval in the medical domain. (2013)

Abstract 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.
Collections Ireland -> Dublin City University -> PubMed

Full list of authors on original publication

Zdeˇnka Urešová, Aleš Tamchyna, Rudolf Rosa, Martin Popel, Michal Novák, David Mareˇcek, Johannes Leveling, Liadh Kelly, Gareth J. F. Jones, Jaroslava Hlaváˇcová and 4 others

Experts in our system

Johannes Leveling
Dublin City University
Total Publications: 66
Liadh Kelly
Dublin City University
Total Publications: 51
Gareth J. F. Jones
Dublin City University
Total Publications: 297