PEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure
CoRR(2024)
Abstract
In this work, we propose a novel tree-based explanation technique, PEACH
(Pretrained-embedding Explanation Across Contextual and Hierarchical
Structure), that can explain how text-based documents are classified by using
any pretrained contextual embeddings in a tree-based human-interpretable
manner. Note that PEACH can adopt any contextual embeddings of the PLMs as a
training input for the decision tree. Using the proposed PEACH, we perform a
comprehensive analysis of several contextual embeddings on nine different NLP
text classification benchmarks. This analysis demonstrates the flexibility of
the model by applying several PLM contextual embeddings, its attribute
selections, scaling, and clustering methods. Furthermore, we show the utility
of explanations by visualising the feature selection and important trend of
text classification via human-interpretable word-cloud-based trees, which
clearly identify model mistakes and assist in dataset debugging. Besides
interpretability, PEACH outperforms or is similar to those from pretrained
models.
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