Difficulty-Controllable Visual Question Generation

Feng Chen, Jiayuan Xie, Yi Cai, Tao Wang, Qing Li

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.