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.

Procedural Multimodal Documents (PMDs) organize textual instructions and corresponding images step by step. Comprehending PMDs and inducing their representations for the downstream reasoning tasks is designated as Procedural MultiModal Machine Comprehension (M3C). In this study, we approach Procedural M3C at a fine-grained level (compared with existing explorations at a document or sentence level), that is, entity. With delicate consideration, we model entity both in its temporal and cross-modal relation and propose a novel Temporal-Modal Entity Graph (TMEG). Specifically, graph structure is formulated to capture textual and visual entities and trace their temporal-modal evolution. In addition, a graph aggregation module is introduced to conduct graph encoding and reasoning. Comprehensive experiments across three Procedural M3C tasks are conducted on a traditional dataset RecipeQA and our new dataset CraftQA, which can better evaluate the generalization of TMEG.