TY - GEN
T1 - Difficulty-Controllable Visual Question Generation
AU - Chen, Feng
AU - Xie, Jiayuan
AU - Cai, Yi
AU - Wang, Tao
AU - Li, Qing
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - Difficulty controllable
KW - Multimodal
KW - Visual question generation
UR - https://www.scopus.com/pages/publications/85115192562
U2 - 10.1007/978-3-030-85896-4_26
DO - 10.1007/978-3-030-85896-4_26
M3 - Conference article published in proceeding or book
AN - SCOPUS:85115192562
SN - 9783030858957
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 332
EP - 347
BT - Web and Big Data - 5th International Joint Conference, APWeb-WAIM 2021, Proceedings
A2 - U, Leong Hou
A2 - Spaniol, Marc
A2 - Sakurai, Yasushi
A2 - Chen, Junying
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Joint Conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021
Y2 - 23 August 2021 through 25 August 2021
ER -