提升神经析取范式模型的可解释性，解决翻译后性能下降问题。
Disentangling Neural Disjunctive Normal Form Models
- 通过拆分嵌套规则节点，实现知识解耦以保留模型性能。
- 在多分类与多标签任务中，译后性能接近原始模型水平。
- 适合需要逻辑可解释性的符号学习场景，如新谓词发现。
基于神经析取范式（DNF）的模型在无需任务先验知识的情况下，在分类与强化学习中表现出色。然而，训练后的符号化翻译过程因阈值处理导致性能下降，主要原因是未能有效解耦网络权重所表示的知识。为此，本文提出一种新解耦方法：将编码嵌套规则的节点拆分为更小的独立节点，从而更好保持模型性能。在二分类、多分类及多标签任务（包括需谓词发明的任务）上的实验表明，该方法能生成紧凑且可解释的逻辑表达，译后性能显著提升，接近翻译前原模型表现。代码已开源。
原文摘要 · Abstract (English)
Neural Disjunctive Normal Form (DNF) based models are powerful and interpretable approaches to neuro-symbolic learning and have shown promising results in classification and reinforcement learning settings without prior knowledge of the tasks. However, their performance is degraded by the thresholding of the post-training symbolic translation process. We show here that part of the performance degradation during translation is due to its failure to disentangle the learned knowledge represented in the form of the networks' weights. We address this issue by proposing a new disentanglement method; by splitting nodes that encode nested rules into smaller independent nodes, we are able to better preserve the models' performance. Through experiments on binary, multiclass, and multilabel classification tasks (including those requiring predicate invention), we demonstrate that our disentanglement method provides compact and interpretable logical representations for the neural DNF-based models, with performance closer to that of their pre-translation counterparts. Our code is available at https://github.com/kittykg/disentangling-ndnf-classification.
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