Conversational AI
NLP
Situated and Interactive Multimodal Conversations
December 8, 2020
Abstract
Next generation virtual assistants are envisioned to handle multimodal inputs (e.g., vision, memories of previous interactions, and the user’s utterances), and perform multimodal actions (e.g., displaying a route while 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) collected 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. Datasets include multimodal context of the items appearing in each scene, and contextual NLU, NLG 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, response generation, and dialog state tracking. We benchmark a collection of existing models on these SIMMC tasks as strong baselines, and demonstrate rich multimodal conversational interactions. Our data, annotations, and models are publicly available.
AUTHORS
Written by
Seungwhan Moon
Paul A. Crook
Ankita De
Shivani Poddar
Theodore Levin
David Whitney
Daniel Difranco
Ahmad Beirami
Eunjoon Cho
Rajen Subba
Alborz Geramifard
Publisher
International Conference on Computational Linguistics (COLING) 2020
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