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
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.
Metadata
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 Institutes 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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