  Building an interactive artificial intelligence that can ask questions about
the real world is one of the biggest challenges for vision and language
problems. In particular, goal-oriented visual dialogue, where the aim of the
agent is to seek information by asking questions during a turn-taking dialogue,
has been gaining scholarly attention recently. While several existing models
based on the GuessWhat?! dataset have been proposed, the Questioner typically
asks simple category-based questions or absolute spatial questions. This might
be problematic for complex scenes where the objects share attributes or in
cases where descriptive questions are required to distinguish objects. In this
paper, we propose a novel Questioner architecture, called Unified Questioner
Transformer (UniQer), for descriptive question generation with referring
expressions. In addition, we build a goal-oriented visual dialogue task called
CLEVR Ask. It synthesizes complex scenes that require the Questioner to
generate descriptive questions. We train our model with two variants of CLEVR
Ask datasets. The results of the quantitative and qualitative evaluations show
that UniQer outperforms the baseline.
