The black-box nature of deep learning models in NLP hinders their widespread
application. The research focus has shifted to Hierarchical Attribution (HA)
for its ability to model feature interactions. Recent works model
non-contiguous combinations with a time-costly greedy search in Eculidean
spaces, neglecting underlying linguistic information in feature
representations. In this work, we introduce a novel method, namely Poincar\'e
Explanation (PE), for modeling feature interactions using hyperbolic spaces in
an $O(n^2logn)$ time complexity. Inspired by Poincar\'e model, we propose a
framework to project the embeddings into hyperbolic spaces, which exhibit
better inductive biases for syntax and semantic hierarchical structures.
Eventually, we prove that the hierarchical clustering process in the projected
space could be viewed as building a minimum spanning tree and propose a time
efficient algorithm. Experimental results demonstrate the effectiveness of our
approach.

该研究介绍了一种称为 Poincaré 解释（PE）的新方法，在 O (n^2logn) 时间复杂度内使用双曲空间建模特征交互，并证明了在投影空间中的层次聚类过程可以视为构建最小生成树，并提出了一种时间高效的算法。实验结果证明了我们方法的有效性。