Classifying community QA questions that contain an image

Kenta Tamaki*, Riku Togashi, Sosuke Kato, Sumio Fujita, Hideyuki Maeda, Tetsuya Sakai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationICTIR 2018 - Proceedings of the 2018 ACM SIGIR International Conference on the Theory of Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages219-222
Number of pages4
ISBN (Electronic)9781450356565
DOIs
Publication statusPublished - 2018 Sep 10
Event8th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2018 - Tianjin, China
Duration: 2018 Sep 142018 Sep 17

Publication series

NameICTIR 2018 - Proceedings of the 2018 ACM SIGIR International Conference on the Theory of Information Retrieval

Conference

Conference8th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2018
Country/TerritoryChina
CityTianjin
Period18/9/1418/9/17

Keywords

  • community question answering
  • convolutional neural networks
  • question categorisation

ASJC Scopus subject areas

  • Information Systems
  • Computer Science (miscellaneous)

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