  Answerer in Questioner's Mind (AQM) is an information-theoretic framework
that has been recently proposed for task-oriented dialog systems. AQM benefits
from asking a question that would maximize the information gain when it is
asked. However, due to its intrinsic nature of explicitly calculating the
information gain, AQM has a limitation when the solution space is very large.
To address this, we propose AQM+ that can deal with a large-scale problem and
ask a question that is more coherent to the current context of the dialog. We
evaluate our method on GuessWhich, a challenging task-oriented visual dialog
problem, where the number of candidate classes is near 10K. Our experimental
results and ablation studies show that AQM+ outperforms the state-of-the-art
models by a remarkable margin with a reasonable approximation. In particular,
the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while
the comparative algorithms diminish the error by less than 6%. Based on our
results, we argue that AQM+ is a general task-oriented dialog algorithm that
can be applied for non-yes-or-no responses.
