Noisy and Unbalanced Multimodal Document Classification: Textbook Exercises as a Use Case

20TH INTERNATIONAL CONFERENCE ON CONTENT-BASED MULTIMEDIA INDEXING, CBMI 2023(2023)

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摘要
In order to foster inclusive education, automatic systems that can adapt textbooks to make them accessible to children with Developmental Coordination Disorder (DCD) are necessary. In this context, we propose a task to classify exercises according to their DCD adaptation type. We introduce a challenging exercise dataset extracted from French textbooks, with two major difficulties: limited and unbalanced, noisy data. To set a baseline on the dataset, we use state-of-the-art models combined through early and late fusion techniques to take advantage of text and vision/layout modalities. Our approach achieves an overall accuracy of 0.802. However, the experiments show the difficulty of the task, especially for minority classes, where the accuracy drops to 0.583.
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关键词
multimodal document classification,noisy data,textbook adaptation,unbalanced data
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