  Visual dialog is a challenging vision-language task in which a series of
questions visually grounded by a given image are answered. To resolve the
visual dialog task, a high-level understanding of various multimodal inputs
(e.g., question, dialog history, and image) is required. Specifically, it is
necessary for an agent to 1) determine the semantic intent of question and 2)
align question-relevant textual and visual contents among heterogeneous
modality inputs. In this paper, we propose Multi-View Attention Network (MVAN),
which leverages multiple views about heterogeneous inputs based on attention
mechanisms. MVAN effectively captures the question-relevant information from
the dialog history with two complementary modules (i.e., Topic Aggregation and
Context Matching), and builds multimodal representations through sequential
alignment processes (i.e., Modality Alignment). Experimental results on VisDial
v1.0 dataset show the effectiveness of our proposed model, which outperforms
the previous state-of-the-art methods with respect to all evaluation metrics.
