@inproceedings{long-etal-2023-improving,
    title = "Improving Situated Conversational Agents with Step-by-Step Multi-modal Logic Reasoning",
    author = "Long, Yuxing  and
      Zhang, Huibin  and
      Hui, Binyuan  and
      Yang, Zhenglu  and
      Yuan, Caixia  and
      Wang, Xiaojie  and
      Huang, Fei  and
      Li, Yongbin",
    editor = "Chen, Yun-Nung  and
      Crook, Paul  and
      Galley, Michel  and
      Ghazarian, Sarik  and
      Gunasekara, Chulaka  and
      Gupta, Raghav  and
      Hedayatnia, Behnam  and
      Kottur, Satwik  and
      Moon, Seungwhan  and
      Zhang, Chen",
    booktitle = "Proceedings of The Eleventh Dialog System Technology Challenge",
    month = sep,
    year = "2023",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.dstc-1.3",
    pages = "15--24",
    abstract = "To fulfill complex user requirements in a situated conversational scenario, the agent needs to conduct step-by-step multi-modal logic reasoning, which includes locating objects, querying information and searching objects. However, existing methods omit this multi-step procedure and therefore constitutes the risk of shortcuts when making predictions. For example, they may directly copy the information from the dialogue history or simply use the textual description without perform visual reasoning. To address this issue and further boost the system performance, we apply the dual process theory to plug a reasoner into the original transformer based model for step-by-step reasoning. When system 2 completes multi-step reasoning, its output is regarded as final prediction. Our proposed method achieved the 1st rank on the summing scores across all four DSTC-11 SIMMC 2.1 sub-tasks.",
}