通过剪枝去除冗余词汇，让可解释模型更易读懂。
Pruning Literals for Highly Efficient Explainability at Word Level
- 对命题逻辑中的文字进行后处理剪枝，简化解释结构。
- 在YELP-HAT数据集上，解释与人类注意力更匹配，准确率提升4%-9%。
- 适合需要透明且简洁解释的NLP应用场景。
随着先进机器学习模型在自然语言处理中广泛应用，其解释能力不足成为关键问题。尽管命题机（Tsetlin Machine, TM）能提供词级解释，但其复杂命题组合仍难被人理解。本文提出一种后处理剪枝方法，移除条款中随机分布的文字，使模型解释更高效可读。在公开数据集YELP-HAT上的实验表明，剪枝后的TM注意力图与人类注意力图的对齐度更高，且成对相似性优于基于注意力的神经网络模型。在准确率方面，该方法未显著降低性能，反而在部分测试数据上提升4%至9%。
原文摘要 · Abstract (English)
Designing an explainable model becomes crucial now for Natural Language Processing(NLP) since most of the state-of-the-art machine learning models provide a limited explanation for the prediction. In the spectrum of an explainable model, Tsetlin Machine(TM) is promising because of its capability of providing word-level explanation using proposition logic. However, concern rises over the elaborated combination of literals (propositional logic) in the clause that makes the model difficult for humans to comprehend, despite having a transparent learning process. In this paper, we design a post-hoc pruning of clauses that eliminate the randomly placed literals in the clause thereby making the model more efficiently interpretable than the vanilla TM. Experiments on the publicly available YELP-HAT Dataset demonstrate that the proposed pruned TM's attention map aligns more with the human attention map than the vanilla TM's attention map. In addition, the pairwise similarity measure also surpasses the attention map-based neural network models. In terms of accuracy, the proposed pruning method does not degrade the accuracy significantly but rather enhances the performance up to 4% to 9% in some test data.
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