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Difficulty-Controllable Visual Question Generation

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12858)

Abstract

Visual Question Generation (VQG) aims to generate questions from images. Existing studies on this topic focus on generating questions solely based on images while neglecting the difficulty of questions. However, to engage users, an automated question generator should produce questions with a level of difficulty that are tailored to a user’s capabilities and experience. In this paper, we propose a Difficulty-controllable Generation Network (DGN) to alleviate this limitation. We borrow difficulty index from education area to define a difficulty variable for representing the difficulty of questions, and fuse it into our model to guide the difficulty-controllable question generation. Experimental results demonstrate that our proposed model not only achieves significant improvements on several automatic evaluation metrics, but also can generate difficulty-controllable questions.

Keywords

  • Difficulty controllable
  • Visual question generation
  • Multimodal

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 62076100), National Key Research and Development Program of China (Standard knowledge graph for epidemic prevention and production recovering intelligent service platform and its applications), the Fundamental Research Funds for the Central Universities, SCUT (No. D2201300, D2210010), the Science and Technology Programs of Guangzhou (201902010046), the Science and Technology Planning Project of Guangdong Province (No. 2020B0101100002).

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Chen, F., Xie, J., Cai, Y., Wang, T., Li, Q. (2021). Difficulty-Controllable Visual Question Generation. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12858. Springer, Cham. https://doi.org/10.1007/978-3-030-85896-4_26

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  • DOI: https://doi.org/10.1007/978-3-030-85896-4_26

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