  Current studies in extractive question answering (EQA) have modeled
single-span extraction setting, where a single answer span is a label to
predict for a given question-passage pair. This setting is natural for general
domain EQA as the majority of the questions in the general domain can be
answered with a single span. Following general domain EQA models, current
biomedical EQA (BioEQA) models utilize single-span extraction setting with
post-processing steps. In this paper, we investigate the difference of the
question distribution across the general and biomedical domains and discover
biomedical questions are more likely to require list-type answers (multiple
answers) than factoid-type answers (single answer). In real-world use cases,
this emphasizes the need for Biomedical EQA models able to handle multiple
question types. Based on this preliminary study, we propose a multi-span
extraction setting, namely sequence tagging approach for BioEQA, which directly
tackles questions with a variable number of phrases as their answer. Our
approach can learn to decide the number of answers for a question from training
data. Our experimental result on the BioASQ 7b and 8b list-type questions
outperformed the best-performing existing models without requiring
post-processing steps.
