Commonsense Reasoning: \protect \@normalcr how do Neuro-Symbolic and Neuro-only approaches compare?

The representation of knowledge is a central task in Artificial Intelligence and has been an active topic of research since the beginnings of the field. Intensive research and labor has been put into producing resources which encode knowledge regarding different topics, structured in suitable formats so as to allow robust, automated reasoning over them. In Natural Language Processing, deep learning models are commonly given unstructured data and seek to learn the necessary knowledge and abstractions required to represent and understand the underlying mechanisms that govern the target language processing tasks. A popular method to address this issue is to expand the training process to include more tasks and data. Yet, it remains one of the challenges of deep learning. In this respect, a promising research path is to combine the rich knowledge encoded in structured resources with deep learning methods, enhancing them with the necessary means to more effectively learn the complexities of the target tasks. In this paper we set out to compare a Neuro-Symbolic model with mainstream Neuro-only models when they are tasked with solving commonsense reasoning problems, which heavily rely on appropriately represented knowledge: commonsense reasoning is an essential part of the human experience, encompassing human values and needs, and by resorting to it, we can organize sensible arguments and decide on effective actions. The results obtained indicate that there is no clear advantage to either approach, with the Neuro-Symbolic model being competitive amongst the Neuro-only models, but not superior.