  Effective communication between humans and intelligent agents has promising
applications for solving complex problems. One such approach is visual
dialogue, which leverages multimodal context to assist humans. However,
real-world scenarios occasionally involve human mistakes, which can cause
intelligent agents to fail. While most prior research assumes perfect answers
from human interlocutors, we focus on a setting where the agent points out
unintentional mistakes for the interlocutor to review, better reflecting
real-world situations. In this paper, we show that human answer mistakes depend
on question type and QA turn in the visual dialogue by analyzing a previously
unused data collection of human mistakes. We demonstrate the effectiveness of
those factors for the model's accuracy in a pointing-human-mistake task through
experiments using a simple MLP model and a Visual Language Model.
