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LLQA-Lifelog question answering dataset

Tran, Ly-Duyen ORCID: 0000-0003-2903-3968, Ho, Thanh Cong, Pham, Lan Anh, Nguyen, Binh, Gurrin, Cathal ORCID: 0000-0003-2903-3968 and Zhou, Liting ORCID: 0000-0002-7778-8743 (2022) LLQA-Lifelog question answering dataset. In: 28th International Conference on Multimedia Modeling: MMM 2022, 6 - 10 June 2022, Phu Quoc, Vietnam. ISBN 978-3-030-98358-1

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Abstract

Recollecting details from lifelog data involves a higher level of granularity and reasoning than a conventional lifelog retrieval task. Investigating the task of Question Answering (QA) in lifelog data could help in human memory recollection, as well as improve traditional lifelog retrieval systems. However, there has not yet been a standardised benchmark dataset for the lifelog-based QA. In order to provide a first dataset and baseline benchmark for QA on lifelog data, we present a novel dataset, LLQA, which is an augmented 85-day lifelog collection and includes over 15,000 multiple-choice questions. We also provide different baselines for the evaluation of future works. The results showed that lifelog QA is a challenging task that requires more exploration. The dataset is publicly available at https://github.com/allie-tran/LLQA.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Lifelogging, Question answering
Subjects:Computer Science > Artificial intelligence
Computer Science > Information retrieval
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > d-real
Published in: MultiMedia Modeling: 28th International Conference, MMM 2022. Lecture Notes in Computer Science 13141. Springer Cham. ISBN 978-3-030-98358-1
Publisher:Springer Cham
Official URL:https://doi.org/10.1007/978-3-030-98358-1
Copyright Information:© 2022 Springer Nature Switzerland AG
Funders:Science Foundation Ireland under grant agreement 13/RC/2106_P2, Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224
ID Code:27300
Deposited On:05 Jul 2022 13:51 by Ly Duyen Tran . Last Modified 05 Jul 2022 14:06

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