  The visual dialog task requires an AI agent to interact with humans in
multi-round dialogs based on a visual environment. As a common linguistic
phenomenon, pronouns are often used in dialogs to improve the communication
efficiency. As a result, resolving pronouns (i.e., grounding pronouns to the
noun phrases they refer to) is an essential step towards understanding dialogs.
In this paper, we propose VD-PCR, a novel framework to improve Visual Dialog
understanding with Pronoun Coreference Resolution in both implicit and explicit
ways. First, to implicitly help models understand pronouns, we design novel
methods to perform the joint training of the pronoun coreference resolution and
visual dialog tasks. Second, after observing that the coreference relationship
of pronouns and their referents indicates the relevance between dialog rounds,
we propose to explicitly prune the irrelevant history rounds in visual dialog
models' input. With pruned input, the models can focus on relevant dialog
history and ignore the distraction in the irrelevant one. With the proposed
implicit and explicit methods, VD-PCR achieves state-of-the-art experimental
results on the VisDial dataset.
