  With the recent advancements in AI, Intelligent Virtual Assistants (IVA) have
become a ubiquitous part of every home. Going forward, we are witnessing a
confluence of vision, speech and dialog system technologies that are enabling
the IVAs to learn audio-visual groundings of utterances and have conversations
with users about the objects, activities and events surrounding them. As a part
of the 7th Dialog System Technology Challenges (DSTC7), for Audio Visual
Scene-Aware Dialog (AVSD) track, We explore `topics' of the dialog as an
important contextual feature into the architecture along with explorations
around multimodal Attention. We also incorporate an end-to-end audio
classification ConvNet, AclNet, into our models. We present detailed analysis
of the experiments and show that some of our model variations outperform the
baseline system presented for this task.

Computation and Language