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 Poincare Explanation (PE), for modeling feature interactions with hyperbolic spaces in a time efficient manner. Specifically, we take building text hierarchies as finding spanning trees in hyperbolic spaces. First we project the embeddings into hyperbolic spaces to elicit inherit semantic and syntax hierarchical structures. Then we propose a simple yet effective strategy to calculate Shapley score. Finally we build the the hierarchy with proving the constructing process in the projected space could be viewed as building a minimum spanning tree and introduce a time efficient building algorithm. Experimental results demonstrate the effectiveness of our approach.

翻译：深度学习模型在自然语言处理中的黑箱特性阻碍了其广泛应用。研究焦点已转向层级归因（Hierarchical Attribution, HA），因其能够建模特征交互。近期研究在欧氏空间中通过耗时的贪心搜索建模非连续组合，忽略了特征表示中蕴含的语言信息。本文提出一种名为庞加莱解释（Poincaré Explanation, PE）的新方法，利用双曲空间高效建模特征交互。具体而言，我们将构建文本层次视为在双曲空间中寻找生成树。首先，将嵌入投影至双曲空间以提取其固有的语义与句法层级结构；随后提出一种简单有效的策略计算沙普利值；最后，通过证明投影空间中的构建过程可视为构建最小生成树，引入一种高效的构建算法。实验结果验证了该方法的有效性。