The importance of sharing patient-generated clinical speech and language data

Kathleen C. Fraser, Nicklas Linz, Hali Lindsay, Alexandra König


Abstract
Increased access to large datasets has driven progress in NLP. However, most computational studies of clinically-validated, patient-generated speech and language involve very few datapoints, as such data are difficult (and expensive) to collect. In this position paper, we argue that we must find ways to promote data sharing across research groups, in order to build datasets of a more appropriate size for NLP and machine learning analysis. We review the benefits and challenges of sharing clinical language data, and suggest several concrete actions by both clinical and NLP researchers to encourage multi-site and multi-disciplinary data sharing. We also propose the creation of a collaborative data sharing platform, to allow NLP researchers to take a more active responsibility for data transcription, annotation, and curation.
Anthology ID:
W19-3007
Volume:
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
CLPsych | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
55–61
Language:
URL:
https://www.aclweb.org/anthology/W19-3007
DOI:
10.18653/v1/W19-3007
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3007.pdf