Hierarchical Interpretation of Neural Text Classification

Abstract

Recent years have witnessed increasing interests in developing interpretable models in Natural Language Processing (NLP). Most existing models aim at identifying input features such as words or phrases important for model predictions. Neural models developed in NLP however often compose word semantics in a hierarchical manner. Interpretation by words or phrases only thus cannot faithfully explain model decisions. This paper proposes a novel Hierarchical INTerpretable neural text classifier, called Hint, which can automatically generate explanations of model predictions in the form of label-associated topics in a hierarchical manner. Model interpretation is no longer at the word level, but built on topics as the basic semantic unit. Experimental results on both review datasets and newsdatasets show that our proposed approach achieves text classification results on par with existing state-of-the-art text classifiers, and generates interpretations more faithful to model predictions and better understood by humans than other interpretable neural text classifiers.

Global reconstruction of language models with linguistic rules – Explainable AI for online consumer reviews

“…Here, we focus on global reconstructions of BERT's predictions for token-level classifications in this work, since this constitutes popular application scenarios of BERT (e.g., AS1, AS3) and since BERT also establishes text representations based on tokens. Moreover, as Zafar et al (2021) and Yan et al (2022) indicate, a reconstruction approach for token-level classifications can also serve as a basis for reconstructions of coarser classification tasks, for instance, for sentence-level classifications (e.g., AS2, AS4).…”