SIMMC: Situated Interactive Multi-Modal Conversational Data Collection And Evaluation Platform
This provides a tool for the Conversational AI research community to collect and evaluate situated interactions, but it is incremental as it extends existing frameworks.
The authors tackled the need for understanding user behavior with digital assistants by introducing SIMMC, a platform for multi-modal conversational data collection and evaluation, which simulates immersive environments and supports both human and AI agents.
As digital virtual assistants become ubiquitous, it becomes increasingly important to understand the situated behaviour of users as they interact with these assistants. To this end, we introduce SIMMC, an extension to ParlAI for multi-modal 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.