Putting reasons back into reasoning: how genuine reasoning is inference-based and why neuro-symbolic NLI could achieve it

Taking Leibniz' ideal of a universal truth-calculating machine as a vantage point, this article provides a philosophically sound analysis of the concept of reasoning in NLP. It argues that reasoning always involves inference, which in turn requires being guided by reason relations. Based on this, the article argues that Symbolic NLP is unable to reason for epistemic reasons on the part of humans, that Neural NLP is likely unable to do so in principle, and that Neuro-Symbolic NLP is ideally set up to succeed where the two approaches in isolation failed, that is, to progress toward realizing Leibniz' vision of a truth-calculating machine—to the extent to which this is possible. We conclude by providing a theoretical grounding for the latter claim in the philosophy of a contemporary rationalist, namely Robert Brandom.