Abstract
We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions. We collect and present an associated dataset, CHARDat, consisting of explanations about 52 health conditions across three clinical dimensions. We conduct extensive experiments using BART and T5 along with data augmentation, and perform automatic, human, and qualitative analyses. We show that while our models can perform decently, CHARD is very challenging with strong potential for further exploration.
Cite
CITATION STYLE
Feng, S. Y., Khetan, V., Sacaleanu, B., Gershman, A., & Hovy, E. (2023). CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 313–327). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.eacl-main.24
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