Explainable Assessment of Healthcare Articles with QA

Alodie Boissonnet, Marzieh Saeidi, Vassilis Plachouras, Andreas Vlachos


Abstract
The healthcare domain suffers from the spread of poor quality articles on the Internet. While manual efforts exist to check the quality of online healthcare articles, they are not sufficient to assess all those in circulation. Such quality assessment can be automated as a text classification task, however, explanations for the labels are necessary for the users to trust the model predictions. While current explainable systems tackle explanation generation as summarization, we propose a new approach based on question answering (QA) that allows us to generate explanations for multiple criteria using a single model. We show that this QA-based approach is competitive with the current state-of-the-art, and complements summarization-based models for explainable quality assessment. We also introduce a human evaluation protocol more appropriate than automatic metrics for the evaluation of explanation generation models.
Anthology ID:
2022.bionlp-1.1
Volume:
Proceedings of the 21st Workshop on Biomedical Language Processing
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–9
Language:
URL:
https://aclanthology.org/2022.bionlp-1.1
DOI:
10.18653/v1/2022.bionlp-1.1
Bibkey:
Cite (ACL):
Alodie Boissonnet, Marzieh Saeidi, Vassilis Plachouras, and Andreas Vlachos. 2022. Explainable Assessment of Healthcare Articles with QA. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 1–9, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Explainable Assessment of Healthcare Articles with QA (Boissonnet et al., BioNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bionlp-1.1.pdf