Prior work on training generative Visual Dialog models with reinforcement
learning(Das et al.) has explored a Qbot-Abot image-guessing game and shown
that this 'self-talk' approach can lead to improved performance at the
downstream dialog-conditioned image-guessing task. However, this improvement
saturates and starts degrading after a few rounds of interaction, and does not
lead to a better Visual Dialog model. We find that this is due in part to
repeated interactions between Qbot and Abot during self-talk, which are not
informative with respect to the image. To improve this, we devise a simple
auxiliary objective that incentivizes Qbot to ask diverse questions, thus
reducing repetitions and in turn enabling Abot to explore a larger state space
during RL ie. be exposed to more visual concepts to talk about, and varied
questions to answer. We evaluate our approach via a host of automatic metrics
and human studies, and demonstrate that it leads to better dialog, ie. dialog
that is more diverse (ie. less repetitive), consistent (ie. has fewer
conflicting exchanges), fluent (ie. more human-like),and detailed, while still
being comparably image-relevant as prior work and ablations.

通过辅助目标激励 Qbot 多样性提问以减少重复，从而达到更好的视觉对话效果，在保证与前期研究同等图像相关性的同时，大大提升了对话的多样性、一致性、流畅度和细节。