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

Community-Based Question Answering via Heterogeneous Social Network Learning

AAAI 2016 · Vision-language and multimodal › Visual question answering · approach: Universal VQA models · 91 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
HSNL (content LSTM + heterogeneous social random walk) instead of CNTNcQA answer ranking · Quora cQA, 80% trainingnDCG: 0.9101 -> 0.9481Helped
HSNL instead of S-matrixcQA answer ranking · Quora cQA, 80% trainingPrecision@1: 0.5755 -> 0.6701Helped
HSNL instead of CNTNcQA answer ranking · Quora cQA, 20% trainingAccuracy: 0.2295 -> 0.3645Helped
add bilinear similarity matrix M (ablation)HSNL-sim · Quora cQA, 80% trainingAccuracy: 0.5556 -> 0.6928Helped
add bilinear similarity matrix M (ablation)HSNL-sim · Quora cQA, 20% trainingnDCG: 0.5721 -> 0.6286Helped
add content LSTM and rating-aware ranking loss to DeepWalk (ablation)DeepWalk · Quora cQA, 80% trainingPrecision@1: 0.4065 -> 0.6701Helped
supervised content models (CNTN) instead of unsupervised BOWcQA · Quora cQA, 20% trainingnDCG: 0.6268 -> 0.5367Hurt

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