Learning to Generate Explanation from e-Hospital Services for Medical Suggestion

Wei-Lin Chen, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen


Abstract
Explaining the reasoning of neural models has attracted attention in recent years. Providing highly-accessible and comprehensible explanations in natural language is useful for humans to understand model’s prediction results. In this work, we present a pilot study to investigate explanation generation with a narrative and causal structure for the scenario of health consulting. Our model generates a medical suggestion regarding the patient’s concern and provides an explanation as the outline of the reasoning. To align the generated explanation with the suggestion, we propose a novel discourse-aware mechanism with multi-task learning. Experimental results show that our model achieves promising performances in both quantitative and human evaluation.
Anthology ID:
2022.coling-1.260
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2946–2951
Language:
URL:
https://aclanthology.org/2022.coling-1.260
DOI:
Bibkey:
Cite (ACL):
Wei-Lin Chen, An-Zi Yen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2022. Learning to Generate Explanation from e-Hospital Services for Medical Suggestion. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2946–2951, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Learning to Generate Explanation from e-Hospital Services for Medical Suggestion (Chen et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.260.pdf
Code
 ntunlplab/tw-eh