论文标题
在不连接规则的情况下生成文本分类的层次解释
Generating Hierarchical Explanations on Text Classification Without Connecting Rules
论文作者
论文摘要
深NLP模型的不透明性激发了解释模型预测的方法的发展。最近，工作引入了层次归因，该归因产生单词的层次聚类，以及每个群集的归因分数。但是，有关层次归因的现有工作均遵循连接规则，将群集限制为输入文本中的连续跨度。我们认为，连接规则作为额外的先前可能会破坏忠实反映模型决策过程的能力。为此，我们建议在没有连接规则的情况下生成层次解释，并引入一个用于生成层次簇的框架。实验结果和进一步的分析表明，该方法在反映模型预测过程的高质量解释方面的有效性。
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.
