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Tackling Situated Multi-Modal Task-Oriented Dialogs with a Single Transformer Model

The Situated Interactive Multi-Modal Conversations (SIMMC) 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs, i.e. visual appearances of objects and user utterances. It consists of four subtasks, multi-modal disambiguation (MM-Disamb), multi-modal coreference resolution (MM-Coref), multi-modal dialog state tracking (MM-DST), and response retrieval and generation. While many task-oriented dialog systems usually tackle each subtask separately, we propose a jointly learned encoder-decoder that performs all four subtasks at once for efficiency. Moreover, we handle the multi-modality of the challenge by representing visual objects as special tokens whose joint embedding is learned via auxiliary tasks. This approach won the MM-Coref and response retrieval subtasks and nominated runner-up for the remaining subtasks using a single unified model. In particular, our model achieved 81.5\% MRR, 71.2\% R@1, 95.0\% R@5, 98.2\% R@10, and 1.9 mean rank in response retrieval task, setting a high bar for the state-of-the-art result in the SIMMC 2.0 track of the Dialog Systems Technology Challenge 10 (DSTC10).

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