A vast amount of biomedical information is available in the form of scientific literature and government-authored patient information documents. While English is the most widely used language in many of these sources, there is a need to provide access to health information in languages other than English. Parallel corpora can be leveraged to implement cross-lingual information retrieval or machine translation tools. Herein, we review the extent of parallel corpus coverage in the biomedical domain. Specifically, we perform a scoping review of existing resources and we describe the recent development of new datasets for scientific literature (the EDP dataset and an extension of the Scielo corpus) and clinical trials (the ReBEC corpus). These corpora are currently being used in the biomedical task in the Conference on Machine Translation (WMT’16 and WMT’17), which illustrates their potential for improving and evaluating biomedical machine translation systems. Furthermore, we suggest additional applications for multilingual natural language processing using these resources, and plan to extend resource coverage to additional text genres and language pairs.
@InProceedings{NÉVÉOL18.854, author = {Aurélie Névéol and Antonio Jimeno Yepes and Mariana Neves and Karin Verspoor}, title = "{Parallel Corpora for the Biomedical Domain}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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