Classifying Community QA Questions That Contain an Image
Kenta Tamaki, Riku Togashi, Sosuke Kato, Sumio Fujita, Hideyuki Maeda, Tetsuya Sakai · 2018
We consider the problem of automatically assigning a category to a given question posted to a Community Question Answering (CQA) site, where the question contains not only text but also an image. For example, CQA users may post a photograph of a dress and ask the community "Is this appropriate for a wedding?'' where the appropriate category for this question might be "Manners, Ceremonial occasions.'' We tackle this problem using Convolutional Neural Networks with a DualNet architecture for combining the image and text representations. Our experiments with real data from Yahoo Chiebukuro and crowdsourced gold-standard categories show that the DualNet approach outperforms a text-only baseline ($p=.0000$), a sum-and-product baseline ($p=.0000$), Multimodal Compact Bilinear pooling ($p=.0000$), and a combination of sum-and-product and MCB ($p=.0000$), where the p-values are based on a randomised Tukey Honestly Significant Difference test with $B = 5000$ trials.