  The opaqueness of deep NLP models has motivated the development of methods
for interpreting how deep models predict. Recently, work has introduced
hierarchical attribution, which produces a hierarchical clustering of words,
along with an attribution score for each cluster. However, existing work on
hierarchical attribution all follows the connecting rule, limiting the cluster
to a continuous span in the input text. We argue that the connecting rule as an
additional prior may undermine the ability to reflect the model decision
process faithfully. To this end, we propose to generate hierarchical
explanations without the connecting rule and introduce a framework for
generating hierarchical clusters. Experimental results and further analysis
show the effectiveness of the proposed method in providing high-quality
explanations for reflecting model predicting process.
