Towards Building Large Scale Multimodal Domain-Aware Conversation Systems

Understand

While multimodal conversation agents are gaining importance in several domains such as retail, travel etc., deep learning research in this area has been limited primarily due to the lack of availability of large-scale, open chatlogs.

• To overcome this bottleneck, in this paper we introduce the task of multimodal, domain-aware conversations, and propose the MMD benchmark dataset.
• This dataset was gathered by working in close coordination with large number of domain experts in the retail domain.
• These experts suggested various conversations flows and dialog states which are typically seen in multimodal conversations in the fashion domain.