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Procedural Multimodal Documents (PMDs) organize textual instructions andcorresponding images step by step. Comprehending PMDs and inducing theirrepresentations for the downstream reasoning tasks is designated as ProceduralMultiModal Machine Comprehension (M3C). In this study, we approach ProceduralM3C at a fine-grained level (compared with existing explorations at a documentor sentence level), that is, entity. With delicate consideration, we modelentity both in its temporal and cross-modal relation and propose a novelTemporal-Modal Entity Graph (TMEG). Specifically, graph structure is formulatedto capture textual and visual entities and trace their temporal-modalevolution. In addition, a graph aggregation module is introduced to conductgraph encoding and reasoning. Comprehensive experiments across three ProceduralM3C tasks are conducted on a traditional dataset RecipeQA and our new datasetCraftQA, which can better evaluate the generalization of TMEG.

Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension