Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering

Jialin Wu, Raymond Mooney

Abstract
Most Outside-Knowledge Visual Question Answering (OK-VQA) systems employ a two-stage framework that first retrieves external knowledge given the visual question and then predicts the answer based on the retrieved content. However, the retrieved knowledge is often inadequate. Retrievals are frequently too general and fail to cover specific knowledge needed to answer the question. Also, the naturally available supervision (whether the passage contains the correct answer) is weak and does not guarantee question relevancy. To address these issues, we propose an Entity-Focused Retrieval (EnFoRe) model that provides stronger supervision during training and recognizes question-relevant entities to help retrieve more specific knowledge. Experiments show that our EnFoRe model achieves superior retrieval performance on OK-VQA, the currently largest outside-knowledge VQA dataset. We also combine the retrieved knowledge with state-of-the-art VQA models, and achieve a new state-of-the-art performance on OK-VQA.
Anthology ID:
2022.emnlp-main.551
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:
8061–8072
Language:
URL:
https://aclanthology.org/2022.emnlp-main.551
DOI:
10.18653/v1/2022.emnlp-main.551
Bibkey:
Cite (ACL):
Jialin Wu and Raymond Mooney. 2022. Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8061–8072, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering (Wu & Mooney, EMNLP 2022)
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https://preview.aclanthology.org/nschneid-patch-5/2022.emnlp-main.551.pdf
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