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Research map / Vision-language and multimodal / Visual question answering

Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base

NeurIPS 2018 · Vision-language and multimodal › Visual question answering · approach: Open-domain multimodal QA · 86 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use semantic-parsing dialog-to-action model instead of memory-network encoder-decoderD2A vs HRED+KVmem · CSQA overallrecall: 18.40% -> 64.04%Helped
add dialog memory (entities, predicates, action subsequences) (ablation)D2A · CSQA overallrecall: 42.18% -> 64.04%Helped
add dialog memory (ablation)D2A · CSQA Simple Question (Direct)recall: 94.04% -> 93.67%No clear effect
add entity memory (ablation)D2A · CSQA Simple Question (Co-referenced)recall: 37.95% -> 71.31%Helped
add entity memory (ablation)D2A · CSQA Verification (Boolean)accuracy: 18.44% -> 45.05%Helped
add predicate memory (ablation)D2A · CSQA Simple Question (Ellipsis)recall: 15.35% -> 86.58%Helped
add action subsequence memory (ablation)D2A · CSQA Logical Reasoning (All)recall: 38.69% -> 42.49%Helped
add action subsequence memory (ablation)D2A · CSQA overallrecall: 64.02% -> 64.04%No clear effect
add action subsequence memory (ablation)D2A · CSQA Verification (Boolean)accuracy: 50.84% -> 45.05%Hurt
add dialog memoryD2A vs HRED+KVmem · CSQA Clarificationrecall: 25.09% -> 19.36%Hurt

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