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

By Pavel Pecina, Ondrej Dusek, Lorraine Goeuriot, Jan Hajic, Jaroslava Hlavácová, Gareth J. F. Jones, Liadh Kelly, Johannes Leveling, David Marecek, Michal Novák and 4 more. 2014

50 citations recorded by the source corpus.

OBJECTIVE 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. METHODS AND DATA 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. RESULTS 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. CONCLUSIONS 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.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology, Natural Language Processing, Machine Translation

How researchers see this paper

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

In Artificial Intelligence

In Information Systems

In Molecular Biology

In Natural Language Processing

In Machine Translation

Citations in the corpus

Findings of the 2014 Workshop on Statistical Machine Translation (2014, methodology): “20 An additional test set for the query translation subtask was adopted from the CLEF 2013 eHealth Task 3 (Pecina et al., 2014).”
Clinical Natural Language Processing in languages other than English: opportunities and challenges (2018, methodology): “[84] [163]* [27, 53, 88] [70]* [80, 124] [36, 106]”
Domain Adaptation for Medical Text Translation using Web Resources (2014, background): “As data in specific domain are usually relatively scarce, the use of web resources to complement the training resources provides an effective way to enhance the SMT systems (Resnik and smith, 2003; Esplà-Gomis and Forcada, 2010; Pecina et al., 2011; Pecina et al., 2012; Pecina et al., 2014).”