Location-Aware Visual Question Generation with Lightweight Models

Nicholas Suwono, Justin Chen, Tun Hung, Ting-Hao Huang, I-Bin Liao, Yung-Hui Li, Lun-Wei Ku, Shao-Hua Sun


Abstract
This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location. Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we present a dataset generation pipeline that leverages GPT-4 to produce diverse and sophisticated questions. Then, we aim to learn a lightweight model that can address the LocaVQG task and fit on an edge device, such as a mobile phone. To this end, we propose a method which can reliably generate engaging questions from location-aware information. Our proposed method outperforms baselines regarding human evaluation (e.g., engagement, grounding, coherence) and automatic evaluation metrics (e.g., BERTScore, ROUGE-2). Moreover, we conduct extensive ablation studies to justify our proposed techniques for both generating the dataset and solving the task.
Anthology ID:
2023.emnlp-main.88
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1415–1432
Language:
URL:
https://aclanthology.org/2023.emnlp-main.88
DOI:
10.18653/v1/2023.emnlp-main.88
Bibkey:
Cite (ACL):
Nicholas Suwono, Justin Chen, Tun Hung, Ting-Hao Huang, I-Bin Liao, Yung-Hui Li, Lun-Wei Ku, and Shao-Hua Sun. 2023. Location-Aware Visual Question Generation with Lightweight Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1415–1432, Singapore. Association for Computational Linguistics.
Cite (Informal):
Location-Aware Visual Question Generation with Lightweight Models (Suwono et al., EMNLP 2023)
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