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Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs
2020
Findings of the Association for Computational Linguistics: EMNLP 2020
unpublished
Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights. We present the first study focused on generating natural language rationales across several complex visual reasoning tasks: visual commonsense reasoning, visual-textual entailment, and visual question answering. The key challenge of accurate rationalization is
doi:10.18653/v1/2020.findings-emnlp.253
fatcat:qwnpmjh7hbbflppfcip5yepu4q