Designing Multimodal Datasets for NLP Challenges
This addresses the need for more cognitively aligned datasets in NLP research, though it appears incremental as it builds on existing multimodal dataset work.
The paper tackles the problem of designing multimodal datasets for NLP challenges by proposing to better represent commonsense semantic inferences and align textual and visual information for action/event dynamics. They introduce the Recipe-to-Video Questions (R2VQ) dataset to test competence-based comprehension over a multimodal recipe collection.
In this paper, we argue that the design and development of multimodal datasets for natural language processing (NLP) challenges should be enhanced in two significant respects: to more broadly represent commonsense semantic inferences; and to better reflect the dynamics of actions and events, through a substantive alignment of textual and visual information. We identify challenges and tasks that are reflective of linguistic and cognitive competencies that humans have when speaking and reasoning, rather than merely the performance of systems on isolated tasks. We introduce the distinction between challenge-based tasks and competence-based performance, and describe a diagnostic dataset, Recipe-to-Video Questions (R2VQ), designed for testing competence-based comprehension over a multimodal recipe collection (http://r2vq.org/). The corpus contains detailed annotation supporting such inferencing tasks and facilitating a rich set of question families that we use to evaluate NLP systems.