  Next generation virtual assistants are envisioned to handle multimodal inputs
(e.g., vision, memories of previous interactions, in addition to the user's
utterances), and perform multimodal actions (e.g., displaying a route in
addition to generating the system's utterance). We introduce Situated
Interactive MultiModal Conversations (SIMMC) as a new direction aimed at
training agents that take multimodal actions grounded in a co-evolving
multimodal input context in addition to the dialog history. We provide two
SIMMC datasets totalling ~13K human-human dialogs (~169K utterances) using a
multimodal Wizard-of-Oz (WoZ) setup, on two shopping domains: (a) furniture
(grounded in a shared virtual environment) and, (b) fashion (grounded in an
evolving set of images). We also provide logs of the items appearing in each
scene, and contextual NLU and coreference annotations, using a novel and
unified framework of SIMMC conversational acts for both user and assistant
utterances. Finally, we present several tasks within SIMMC as objective
evaluation protocols, such as Structural API Prediction and Response
Generation. We benchmark a collection of existing models on these SIMMC tasks
as strong baselines, and demonstrate rich multimodal conversational
interactions. Our data, annotations, code, and models are publicly available.
