  With a lot of work about context-free question answering systems, there is an
emerging trend of conversational question answering models in the natural
language processing field. Thanks to the recently collected datasets, including
QuAC and CoQA, there has been more work on conversational question answering,
and recent work has achieved competitive performance on both datasets. However,
to best of our knowledge, two important questions for conversational
comprehension research have not been well studied: 1) How well can the
benchmark dataset reflect models' content understanding? 2) Do the models well
utilize the conversation content when answering questions? To investigate these
questions, we design different training settings, testing settings, as well as
an attack to verify the models' capability of content understanding on QuAC and
CoQA. The experimental results indicate some potential hazards in the benchmark
datasets, QuAC and CoQA, for conversational comprehension research. Our
analysis also sheds light on both what models may learn and how datasets may
bias the models. With deep investigation of the task, it is believed that this
work can benefit the future progress of conversation comprehension. The source
code is available at https://github.com/MiuLab/CQA-Study.
