Little is known on how this learning approach compares to RL not only regarding task success, but also in terms of the quality of the linguistic output, a gap we seek to fill in this paper.
our contributions are: 2 • The introduction of a single visuallygrounded dialogue state encoder jointly trained with the guesser and question generator modules to address a foundational question of how to integrate visual grounding with dialogue system components; this yields up to 9% improvement on task success.