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

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 multidisciplinary 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.