Research

As digital virtual assistants become ubiquitous, it becomes increasingly important to understand the situated behavior of users as they interact with these assistants. To this end, we introduce SIMMC, an extension to ParlAI for multimodal conversational data collection and system evaluation. SIMMC simulates an immersive setup, where crowd workers are able to interact with environments constructed in AI Habitat or Unity while engaging in a conversation. The assistant in SIMMC can be a crowd worker or Artificial Intelligent (AI) agent. This enables both (i) a multi-player / Wizard of Oz setting for data collection, or (ii) a single player mode for model / system evaluation. We plan to open-source a situated conversational data-set collected on this platform for the Conversational AI research community.