  The multi-answer phenomenon, where a question may have multiple answers
scattered in the document, can be well handled by humans but is challenging
enough for machine reading comprehension (MRC) systems. Despite recent progress
in multi-answer MRC, there lacks a systematic analysis of how this phenomenon
arises and how to better address it. In this work, we design a taxonomy to
categorize commonly-seen multi-answer MRC instances, with which we inspect
three multi-answer datasets and analyze where the multi-answer challenge comes
from. We further analyze how well different paradigms of current multi-answer
MRC models deal with different types of multi-answer instances. We find that
some paradigms capture well the key information in the questions while others
better model the relationship between questions and contexts. We thus explore
strategies to make the best of the strengths of different paradigms.
Experiments show that generation models can be a promising platform to
incorporate different paradigms. Our annotations and code are released for
further research.
