A Step from VQA to CQA: Adapting Visual QA Models for Community QA Tasks
Avikalp Srivastava (CMU), HsinWen Liu (Waseda Univ.), Sumio Fujita
自然言語処理 (Natural Language Processing) 画像処理 (Image Processing) 情報検索 (Information Retrieval) 機械学習 (Machine Learning)
- In this paper, we study and develop methods to derive highlevel representations for image-text pairs in image-based community question answering (CQA) for performing tasks of practical significance on such a platform - automated question classification and finding experts for answering a question. Motivated by our aim to also utilize this work as a step towards basic question-answering on image-based CQA, and to utilize the advances in visual question answering models, we analyze the differences between visual QA & community QA datasets, understand the limitations of applying VQA models directly to CQA data and tasks, and make novel augmentations to VQA-inspired models to best exploit the multimodal data from Yahoo! Chiebukuro’s CQA dataset.
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