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Q. Can knowledge graphs be used to answer Boolean questions? A. It’s complicated!

Dzendzik, Daria, Vogel, Carl orcid logoORCID: 0000-0001-8928-8546 and Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 (2020) Q. Can knowledge graphs be used to answer Boolean questions? A. It’s complicated! In: First Workshop on Insights from Negative Results in NLP, 10 Nov 2020, Online.

Abstract
In this paper we explore the problem of machine reading comprehension, focusing on the BoolQ dataset of Yes/No questions. We carry out an error analysis of a BERT-based machine reading comprehension model on this dataset, revealing issues such as unstable model behaviour and some noise within the dataset itself. We then experiment with two approaches for integrating information from knowledge graphs: (i) concatenating knowledge graph triples to text passages and (ii) encoding knowledge with a Graph Neural Network. Neither of these approaches show a clear improvement and we hypothesize that this may be due to a combination of inaccuracies in the knowledge graph, imprecision in entity linking, and the models’ inability to capture additional information from knowledge graphs.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the First Workshop on Insights from Negative Results in NLP. . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:http://dx.doi.org/10.18653/v1/2020.insights-1.2
Copyright Information:© 2020 The Authors. (CC-BY-4.0)
Funders:Science Foundation Ireland in the ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106) and the European Regional Development Fund.
ID Code:25960
Deposited On:03 Jun 2021 11:09 by Jennifer Foster . Last Modified 03 Jun 2021 11:09
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