Multi-VQG: Generating Engaging Questions for Multiple Images
Published in EMNLP, 2022
Recommended citation: Min-Hsuan Yeh, Vicent Chen, Ting-Hao 'Kenneth' Haung, and Lun-Wei Ku. (2022). "Multi-VQG: Generating Engaging Questions for Multiple Images," to appear in Proceedings of the The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://arxiv.org/abs/2211.07441
We propose a new task, Multi-VQG, which aims to generate engaging questions for multiple images. We introduce a new dataset, MVQG, which contains arround 30,000 question and image sequence pairs. We also propose both end-to-end and dual-staged models extended from VL-T5 to generate questions with story information. We evaluate our models on MVQG and show that models with explicit story information yield better results.
Recommended citation: Min-Hsuan Yeh, Vicent Chen, Ting-Hao ‘Kenneth’ Haung, and Lun-Wei Ku. (2022). “Multi-VQG: Generating Engaging Questions for Multiple Images,” to appear in Proceedings of the The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP).