In natural language processing (NLP), deep neural networks (DNNs) could model
complex interactions between context and have achieved impressive results on a
range of NLP tasks. Prior works on feature interaction attribution mainly focus
on studying symmetric interaction that only explains the additional influence
of a set of words in combination, which fails to capture asymmetric influence
that contributes to model prediction. In this work, we propose an asymmetric
feature interaction attribution explanation model that aims to explore
asymmetric higher-order feature interactions in the inference of deep neural
NLP models. By representing our explanation with an directed interaction graph,
we experimentally demonstrate interpretability of the graph to discover
asymmetric feature interactions. Experimental results on two sentiment
classification datasets show the superiority of our model against the
state-of-the-art feature interaction attribution methods in identifying
influential features for model predictions. Our code is available at
this https URL

提出了一种基于深度神经网络的自然语言处理模型的非对称特征交互解释模型，利用有向交互图表示解释，实验结果表明该模型在情感分类数据集上识别有影响的特征方面表现优异。