Advocates for Neuro-Symbolic AI (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, challenges and future directions, and aim to answer the question of whether NeSy is indeed meeting its promises: reasoning, out-of-distribution generalization, interpretability, learning and reasoning from small data, and transferability to new domains. We examine the impact of knowledge representation, such as rules and semantic networks, language structure and relational structure, and whether implicit or explicit reasoning contributes to higher promise scores. We find that knowledge encoded in relational structures and explicit reasoning tend to lead to more NeSy goals being satisfied. We also advocate for a more methodical approach to the application of theories of reasoning, which we hope can reduce some of the friction between the symbolic and sub-symbolic schools of AI.
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