What is it about?
We conduct a structured review of studies implementing NeSy for NLP aiming to answer whether NeSy is 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.
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Why is it important?
We find many discrepancies in how reasoning is defined, specifically in relation to human-level reasoning, which impacts decisions about model architectures and drives 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.
Perspectives
Systems capable of capturing the nuances of natural language (i.e., ones that “understand” human reasoning) while returning sound conclusions (i.e., perform logical reasoning) could help combat some of the most consequential issues of our times such as mis- and dis-information, corporate propaganda such as climate change denialism, divisive political speech, and other harmful rhetoric in the social discourse.
Kyle Hamilton
Dublin Institute of Technology
Read the Original
This page is a summary of: Is neuro-symbolic AI meeting its promises in natural language processing? A structured review, Semantic Web, November 2022, IOS Press,
DOI: 10.3233/sw-223228.
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