  We propose a grounded dialogue state encoder which addresses a foundational
issue on how to integrate visual grounding with dialogue system components. As
a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal
is to identify an object in a complex visual scene by asking a sequence of
yes/no questions. Our visually-grounded encoder leverages synergies between
guessing and asking questions, as it is trained jointly using multi-task
learning. We further enrich our model via a cooperative learning regime. We
show that the introduction of both the joint architecture and cooperative
learning lead to accuracy improvements over the baseline system. We compare our
approach to an alternative system which extends the baseline with reinforcement
learning. Our in-depth analysis shows that the linguistic skills of the two
models differ dramatically, despite approaching comparable performance levels.
This points at the importance of analyzing the linguistic output of competing
systems beyond numeric comparison solely based on task success.
