Dr. Reto Gubelmann

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.

In the field of computational argument quality assessment, logic and dialectic are essential dimensions used to measure the quality of argumentative texts. Both of them have found their way into the field due to their importance to argumentation theory. We trace the development of core logical concepts of validity and soundness from their first use in argumentation theory to their understanding in state-of-the-art research. We show how, in the course of this development, dialectical considerations have taken center stage, at the cost of the logical perspective. Then, we take a closer look at the quality dimensions used in the field of computational argument quality assessment. Based on an analysis of prior empirical work in this field, we show how methodological considerations from argument theory can benefit state-of-the-art methods in computational argument quality assessment. We propose an even clearer separation between the two quality dimensions not only in regards to their definitions, but also in regards to the granularity at which the argumentative text is being annotated and assessed.