@inproceedings{finley-etal-2018-dictations,
title = "From dictations to clinical reports using machine translation",
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",
editor = "Bangalore, Srinivas and
Chu-Carroll, Jennifer and
Li, Yunyao",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
month = jun,
year = "2018",
address = "New Orleans - Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-3015",
doi = "10.18653/v1/N18-3015",
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.",
}
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%0 Conference Proceedings
%T From dictations to clinical reports using machine translation
%A Finley, Gregory
%A Salloum, Wael
%A Sadoughi, Najmeh
%A Edwards, Erik
%A Robinson, Amanda
%A Axtmann, Nico
%A Brenndoerfer, Michael
%A Miller, Mark
%A Suendermann-Oeft, David
%Y Bangalore, Srinivas
%Y Chu-Carroll, Jennifer
%Y Li, Yunyao
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans - Louisiana
%F finley-etal-2018-dictations
%X 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.
%R 10.18653/v1/N18-3015
%U https://aclanthology.org/N18-3015
%U https://doi.org/10.18653/v1/N18-3015
%P 121-128
Markdown (Informal)
[From dictations to clinical reports using machine translation](https://aclanthology.org/N18-3015) (Finley et al., NAACL 2018)
ACL
- Gregory Finley, Wael Salloum, Najmeh Sadoughi, Erik Edwards, Amanda Robinson, Nico Axtmann, Michael Brenndoerfer, Mark Miller, and David Suendermann-Oeft. 2018. From dictations to clinical reports using machine translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 121–128, New Orleans - Louisiana. Association for Computational Linguistics.