PE: A Poincare Explanation Method for Fast Text Hierarchy Generation
Qian Chen Xiaofeng He Hongzhao Li Hongyu Yi
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.