Abstract

Advocates for Neuro-Symbolic Artificial Intelligence (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, with the aim of answering 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 systems where logic is compiled into the neural network lead to the most NeSy goals being satisfied, while other factors such as knowledge representation, or type of neural architecture do not exhibit a clear correlation with goals being met. We find many discrepancies in how reasoning is defined, specifically in relation to human level reasoning, which impact decisions about model architectures and drive conclusions which are not always consistent across studies. Hence we advocate for a more methodical approach to the application of theories of human reasoning as well as the development of appropriate benchmarks, which we hope can lead to a better understanding of progress in the field. We make our data and code available on github for further analysis.1

中文翻译：

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神经符号 AI 是否实现了其在自然语言处理方面的承诺？结构化审查

摘要

神经符号人工智能 (NeSy) 的倡导者断言，将深度学习与符号推理相结合将导致比任何一种范式本身都更强大的人工智能。尽管深度学习取得了成功，但人们普遍认为，即使是我们最好的深度学习系统也不擅长抽象推理。由于推理与语言有着千丝万缕的联系，因此从直觉上讲，自然语言处理 (NLP) 将是 NeSy 的特别合适的候选者。我们对为 NLP 实施 NeSy 的研究进行了结构化审查，目的是回答 NeSy 是否确实实现了其承诺的问题：推理、分布外泛化、可解释性、从小数据中学习和推理，以及向新数据的可迁移性域。我们检查知识表示的影响，例如规则和语义网络、语言结构和关系结构，以及隐式或显式推理是否有助于提高承诺分数。我们发现，将逻辑编译到神经网络中的系统可以满足大多数 NeSy 目标，而知识表示或神经架构类型等其他因素与实现的目标没有明显的相关性。我们发现在如何定义推理方面存在许多差异，特别是与人类水平推理相关的差异，这会影响有关模型架构的决策并得出结论，而这些结论在研究中并不总是一致的。因此，我们提倡采用更有条理的方法来应用人类推理理论以及制定适当的基准，我们希望这可以导致更好地了解该领域的进展。我们在 github 上提供我们的数据和代码以供进一步分析。 1