Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension
This work addresses the challenge of procedural multimodal machine comprehension for applications like instruction understanding, though it is incremental as it builds on existing entity-level approaches.
The paper tackles the problem of fine-grained comprehension of procedural multimodal documents by modeling entities in their temporal and cross-modal relations, proposing a Temporal-Modal Entity Graph (TMEG) that achieves improved performance on tasks like RecipeQA and a new dataset CraftQA.
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.