Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision

Khalil Mrini, Harpreet Singh, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter W. Chang, Emilia Farcas, Ndapa Nakashole


Abstract
Current medical question answering systems have difficulty processing long, detailed and informally worded questions submitted by patients, called Consumer Health Questions (CHQs). To address this issue, we introduce a medical question understanding and answering system with knowledge grounding and semantic self-supervision. Our system is a pipeline that first summarizes a long, medical, user-written question, using a supervised summarization loss. Then, our system performs a two-step retrieval to return answers. The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document. In the absence of labels for question matching or answer relevance, we design 3 novel, self-supervised and semantically-guided losses. We evaluate our model against two strong retrieval-based question answering baselines. Evaluators ask their own questions and rate the answers retrieved by our baselines and own system according to their relevance. They find that our system retrieves more relevant answers, while achieving speeds 20 times faster. Our self-supervised losses also help the summarizer achieve higher scores in ROUGE, as well as in human evaluation metrics.
Anthology ID:
2022.coling-1.241
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:
2734–2747
Language:
URL:
https://aclanthology.org/2022.coling-1.241
DOI:
Bibkey:
Cite (ACL):
Khalil Mrini, Harpreet Singh, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter W. Chang, Emilia Farcas, and Ndapa Nakashole. 2022. Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2734–2747, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision (Mrini et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.241.pdf
Code
 khalilmrini/medical-question-answering