We present research towards bridging the language gap between migrant workers in Qatar and medical staff. In particular, we present the first steps towards the development of a real-world Hindi-English machine translation system for doctor-patient communication. As this is a low-resource language pair, especially for speech and for the medical domain, our initial focus has been on gathering suitable training data from various sources. We applied a variety of methods ranging from fully automatic extraction from the Web to manual annotation of test data. Moreover, we developed a method for automatically augmenting the training data with synthetically generated variants, which yielded a very sizable improvement of more than 3 BLEU points absolute.
@article{arxiv.1610.02633,
title = {Enabling Medical Translation for Low-Resource Languages},
author = {Ahmad Musleh and Nadir Durrani and Irina Temnikova and Preslav Nakov and Stephan Vogel and Osama Alsaad},
journal= {arXiv preprint arXiv:1610.02633},
year = {2016}
}
Comments
CICLING-2016: 17th International Conference on Intelligent Text Processing and Computational Linguistics, Keywords: Machine Translation, medical translation, doctor-patient communication, resource-poor languages, Hindi