  With the recent advancements in Artificial Intelligence (AI), Intelligent
Virtual Assistants (IVA) such as Alexa, Google Home, etc., have become a
ubiquitous part of many homes. Currently, such IVAs are mostly audio-based, but
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. This will enable agents to have conversations with users about
the objects, activities and events surrounding them. In this work, we present
three main architectural explorations for the Audio Visual Scene-Aware Dialog
(AVSD): 1) investigating `topics' of the dialog as an important contextual
feature for the conversation, 2) exploring several multimodal attention
mechanisms during response generation, 3) incorporating an end-to-end audio
classification ConvNet, AclNet, into our architecture. We discuss detailed
analysis of the experimental results and show that our model variations
outperform the baseline system presented for the AVSD task.
