Consumer Health Question Answering Using Off-the-Shelf Components

P. 571–579.

Alexander Pugachev, Artemova E., Bondarenko A., Braslavski P.

In this paper, we address the task of open-domain health question answering (QA). The quality of existing QA systems heavily depends on the annotated data that is often difficult to obtain, especially in the medical domain. To tackle this issue, we opt for PubMed and Wikipedia as trustworthy document collections to retrieve evidence. The questions and retrieved passages are passed to off-the-shelf question answering models, whose predictions are then aggregated into a final score. Thus, our proposed approach is highly data-efficient. Evaluation on 113 health-related yes/no question and answer pairs demonstrates good performance achieving AUC of 0.82.

Keywords: health question answering

Publication based on the results of: Models and method for analysis of unstructured data, data mining and recommender systems (2023)

In book

Advances in Information Retrieval. 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II

Springer, 2023.