Can recent studies which improve our understanding of how the human brain is involved in processing language possibly help inform neural-symbolic language models?
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The use of neuro-symbolic methods to supplement the performance of deep learning based natural language inference models has witnessed a resurgence. In this work, we review three sets of recent results in human cognition experiments – in natural language comprehension, in natural language inference, and in computer program comprehension - a field bearing similarities to natural language. In light of these three works, we discuss the broader role cognitive neuroscience can play in informing the design of neuro-symbolic inference model architectures for language.