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DCU Team at the NTCIR-16 RCIR Task

Nguyen, Manh-Duy orcid logoORCID: 0000-0001-6878-7039, Nguyen, Thao-Nhu orcid logoORCID: 0000-0003-1356-9434, Nguyen, Binh T., Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545 and Cathal, Gurrin orcid logoORCID: 0000-0003-2903-3968 (2022) DCU Team at the NTCIR-16 RCIR Task. In: NTCIR 16 Conference: Proceedings of the 16th NTCIR Conference on Evaluation of Information Access Technologies, 14-17 June 2022, Tokyo, Japan.

Abstract
Reading is one of the most common everyday activities. People read through most of their daily context such as during study or for entertainment in their spare time. Despite playing a critical role in our lives, there has been limited research on how people read and how it affects their level of understanding. The NTCIR-16 RCIR challenge is the first collaborative evaluation that aims to automatically measure the reading comprehension of a reader and integrate it as part of the information retrieval process. In this paper, we present our approach for the NTCIR-16 RCIR challenge, in which task participants are required to predict reading comprehension using eye movement signals of the readers. We utilised several conventional machine learning techniques to estimate the level of comprehension and combined it with a language model to perform text retrieval. Our extensive experiments, covering both subject-dependent and subject-independent scenarios, showed that our approach with fine-tuning obtained a Spearman’s coefficient of 0.5993 for the comprehension-evaluation task and nDCG at 0.7296 for the comprehension-based retrieval task.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:reading comprehension; machine learning; language modelling
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 16th NTCIR Conference on Evaluation of Information Access Technologies. .
Official URL:https://research.nii.ac.jp/ntcir/workshop/OnlinePr...
Copyright Information:© 2022 The Authors
Funders:Science Foundation Ireland 18/CRT/6223, Science Foundation Ireland 12/RC/2289, Science Foundation Ireland 13/RC/2106, Science Foundation Ireland 13/RC/2106_P2, European Regional Development Fund
ID Code:27717
Deposited On:08 Sep 2022 14:27 by Manh Duy Nguyen . Last Modified 08 Sep 2022 14:27
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