@InProceedings{finley-EtAl:2018:N18-3,
  author    = {Finley, Gregory  and  Salloum, Wael  and  Sadoughi, Najmeh  and  Edwards, Erik  and  Robinson, Amanda  and  Axtmann, Nico  and  Brenndoerfer, Michael  and  Miller, Mark  and  Suendermann-Oeft, David},
  title     = {From dictations to clinical reports using machine translation},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans - Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {121--128},
  abstract  = {A typical workflow to document clinical encounters entails dictating a summary, running speech recognition, and post-processing the resulting text into a formatted letter. Post-processing entails a host of transformations including punctuation restoration, truecasing, marking sections and headers, converting dates and numerical expressions, parsing lists, etc. In conventional implementations, most of these tasks are accomplished by individual modules. We introduce a novel holistic approach to post-processing that relies on machine callytranslation. We show how this technique outperforms an alternative conventional system—even learning to correct speech recognition errors during post-processing—while being much simpler to maintain.},
  url       = {http://www.aclweb.org/anthology/N18-3015}
}