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

Context, Attention and Audio Feature Explorations for Audio Visual Scene-Aware Dialog