Question Answering (QA) is a benchmark Natural Language Processing (NLP) task
where models predict the answer for a given question using related documents,
images, knowledge bases and question-answer pairs. Automatic QA has been
successfully applied in various domains like search engines and chatbots.
However, for specific domains like biomedicine, QA systems are still rarely
used in real-life settings. Biomedical QA (BQA), as an emerging QA task,
enables innovative applications to effectively perceive, access and understand
complex biomedical knowledge. In this work, we provide a critical review of
recent efforts in BQA. We comprehensively investigate prior BQA approaches,
which are classified into 6 major methodologies (open-domain, knowledge base,
information retrieval, machine reading comprehension, question entailment and
visual QA), 4 topics of contents (scientific, clinical, consumer health and
examination) and 5 types of formats (yes/no, extraction, generation,
multi-choice and retrieval). In the end, we highlight several key challenges of
BQA and explore potential directions for future works.
Qiao Jin, Zheng Yuan, Guangzhi Xiong, Qianlan Yu, Chuanqi Tan, Mosha Chen, Songfang Huang, Xiaozhong Liu, Sheng Yu