Explainable Document Classification via Pattern Structures

Inherently explainable Machine Learning (ML) models are able to provide explanations for their predictions by virtue of their construction. The explanations of a ML model are more comprehensible if they are expressed in terms of its input features. Our paper proposes an inherently explainable pipeline for document classification using pattern structures and Abstract Meaning Representation (AMR) graphs. The pipeline generates two kinds of explanations: intermediate and final ones, that justify its classifications. Intermediate explanations are represented as significant subgraphs found in the document graphs of test documents. Final explanations are the sentences of the test documents, that correspond to the significant subgraphs.