Model explainability - in Context of Argument Mining

Abstract

Argument mining is a rising research area in natural language processing, the goal of which is to extract argumentative structures from natural language texts. Such components contain a lot of information not only limited to objective questions such as finding the location, etc., but can also answer many subjective questions as to why someone holds this opinion. Argument mining has already been applied in social media platforms, legal, and newspapers as a qualitative assessment tool, providing a powerful tool for analysis to analysts without prior knowledge of the domain. Being such a complex task, little research is done in explaining the state-of-the-art models in this domain. In this project, we are trying to analyze the workings of these models as to why they behave in this way and verify it. We expect to give a combined algorithm that does the above and presents it in an explainable and human-comprehensible format so that users without any prior knowledge can understand the model’s inner workings and verify it according to their respective tasks.