Abstract
Generating engaging content has drawn much recent attention in the NLP community. Asking questions is a natural way to respond to photos and promote awareness. However, most answers to questions in traditional question-answering (QA) datasets are factoids, which reduce individuals’ willingness to answer. Furthermore, traditional visual question generation (VQG) confines the source data for question generation to single images, resulting in a limited ability to comprehend time-series information of the underlying event. In this paper, we propose generating engaging questions from multiple images. We present MVQG, a new dataset, and establish a series of baselines, including both end-to-end and dual-stage architectures. Results show that building stories behind the image sequence enables models togenerate engaging questions, which confirms our assumption that people typically construct a picture of the event in their minds before asking questions. These results open up an exciting challenge for visual-and-language models to implicitly construct a story behind a series of photos to allow for creativity and experience sharing and hence draw attention to downstream applications.- Anthology ID:
- 2022.emnlp-main.19
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 277–290
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.19
- DOI:
- 10.18653/v1/2022.emnlp-main.19
- Cite (ACL):
- Min-Hsuan Yeh, Vincent Chen, Ting-Hao Huang, and Lun-Wei Ku. 2022. Multi-VQG: Generating Engaging Questions for Multiple Images. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 277–290, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Multi-VQG: Generating Engaging Questions for Multiple Images (Yeh et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-main.19.pdf