SHAP values for Explaining CNN-based Text Classification Models
This addresses the need for explainability in NLP models for regulated industries like banking, though it is incremental as it adapts existing SHAP techniques to a specific domain.
The paper tackled the problem of interpreting CNN-based text classification models in NLP by developing a methodology to compute SHAP values for local explainability and extending it to global feature importance, with results demonstrated on sentiment analysis of Amazon Electronic Review data.
Deep neural networks are increasingly used in natural language processing (NLP) models. However, the need to interpret and explain the results from complex algorithms are limiting their widespread adoption in regulated industries such as banking. There has been recent work on interpretability of machine learning algorithms with structured data. But there are only limited techniques for NLP applications where the problem is more challenging due to the size of the vocabulary, high-dimensional nature, and the need to consider textual coherence and language structure. This paper develops a methodology to compute SHAP values for local explainability of CNN-based text classification models. The approach is also extended to compute global scores to assess the importance of features. The results are illustrated on sentiment analysis of Amazon Electronic Review data.