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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2946–2951
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.260
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2022.coling-1.260.pdf
- Code
- ntunlplab/tw-eh