  Humans work together to solve common problems by having discussions,
explaining, and agreeing or disagreeing with each other. Similarly, if a system
can have discussions with humans when solving tasks, it can improve the
system's performance and reliability. In previous research on explainability,
it has only been possible for the system to make predictions and for humans to
ask questions about them rather than having a mutual exchange of opinions. This
research aims to create a dataset and computational framework for systems that
discuss and refine their predictions through dialogue. Through experiments, we
show that the proposed system can have beneficial discussions with humans
improving the accuracy by up to 25 points in the natural language inference
task.
