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Chapter 13. Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
Alessandro Oltramari, Jonathan Francis, Filip Ilievski, Kaixin Ma, Roshanak Mirzaee
294 - 310
10.3233/FAIA210360
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 342: Neuro-Symbolic Artificial Intelligence: The State of the Art
Abstract

This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks. Different methods for integrating neural language models and knowledge graphs are discussed. The situations in which this combination is most appropriate are characterized, including quantitative evaluation and qualitative error analysis on a variety of commonsense question answering benchmark datasets.

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