Exploring the Influence of Dialog Input Format for Unsupervised Clinical Questionnaire Filling

0Citations
Citations of this article
17Readers
Mendeley users who have this article in their library.

Abstract

In the medical field, we have seen the emergence of health-bots that interact with patients to gather data and track their state. One of the downstream application is automatic questionnaire filling, where the content of the dialog is used to automatically fill a pre-defined medical questionnaire. Previous work has shown that answering questions from the dialog context can successfully be cast as a Natural Language Inference (NLI) task and therefore benefit from current pre-trained NLI models. However, NLI models have mostly been trained on text rather than dialogs, which may have an influence on their performance. In this paper, we study the influence of content transformation and content selection on the questionnaire filling task. Our results demonstrate that dialog pre-processing can significantly improve the performance of zero-shot questionnaire filling models which take health-bots dialogs as input.

Cite

CITATION STYLE

APA

Toudeshki, F. G., Liednikova, A., Jolivet, P., & Gardent, C. (2022). Exploring the Influence of Dialog Input Format for Unsupervised Clinical Questionnaire Filling. In LOUHI 2022 - 13th International Workshop on Health Text Mining and Information Analysis, Proceedings of the Workshop (pp. 1–13). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.louhi-1.1

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free