ProZe: Explainable and Prompt-Guided Zero-Shot Text Classification

Abstract

As technology accelerates the generation and communication of textual data, the need to automatically understand this content becomes a necessity. In order to classify text, being it for tagging, indexing, or curating documents, one often relies on large, opaque models that are trained on preannotated datasets, making the process unexplainable, difficult to scale, and ill-adapted for niche domains with scarce data. To tackle these challenges, we propose ProZe, a text classification approach that leverages knowledge from two sources: prompting pretrained language models, as well as querying ConceptNet, a common-sense knowledge base which can be used to add a layer of explainability to the results. We evaluate our approach empirically and we show how this combination not only performs on par with state-of-the-art zero shot classification on several domains, but also offers explainable predictions that can be visualized.