Abstract
In this paper, we explore consumer health question (CHQ) reformulation, focusing on enhancing the quality of reformation of questions without considering interest shifts. Our study introduces the use of the NIH GARD website as a gold standard dataset for this specific task, emphasizing its relevance and applicability. Additionally, we developed other datasets consisting of related questions scraped from Google, Bing, and Yahoo. We augmented, evaluated and analyzed the various datasets, demonstrating that the reformulation task closely resembles the question entailment generation task. Our approach, which integrates the Focus and Type of consumer inquiries, represents a significant advancement in the field of question reformulation. We provide a comprehensive analysis of different methodologies, offering insights into the development of more effective and user-centric AI systems for consumer health support.- Anthology ID:
- 2024.cl4health-1.27
- Volume:
- Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Dina Demner-Fushman, Sophia Ananiadou, Paul Thompson, Brian Ondov
- Venues:
- CL4Health | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 220–228
- Language:
- URL:
- https://aclanthology.org/2024.cl4health-1.27
- DOI:
- Cite (ACL):
- Jooyeon Lee, Luan Huy Pham, and Özlem Uzuner. 2024. Enhancing Consumer Health Question Reformulation: Chain-of-Thought Prompting Integrating Focus, Type, and User Knowledge Level. In Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024, pages 220–228, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Enhancing Consumer Health Question Reformulation: Chain-of-Thought Prompting Integrating Focus, Type, and User Knowledge Level (Lee et al., CL4Health-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.cl4health-1.27.pdf