We build on abduction-based explanations for ma-chine learning and develop a
method for computing local explanations for neural network models in natural
language processing (NLP). Our explanations comprise a subset of the words of
the in-put text that satisfies two key features: optimality w.r.t. a
user-defined cost function, such as the length of explanation, and robustness,
in that they ensure prediction invariance for any bounded perturbation in the
embedding space of the left out words. We present two solution algorithms,
respectively based on implicit hitting sets and maximum universal subsets,
introducing a number of algorithmic improvements to speed up convergence of
hard instances. We show how our method can be con-figured with different
perturbation sets in the em-bedded space and used to detect bias in predictions
by enforcing include/exclude constraints on biased terms, as well as to enhance
existing heuristic-based NLP explanation frameworks such as Anchors. We
evaluate our framework on three widely used sentiment analysis tasks and texts
of up to100words from SST, Twitter and IMDB datasets,demonstrating the
effectiveness of the derived explanations.

本文提出了基于绑架式解释的机器学习方法，为自然语言处理中的神经网络模型计算局部解释。根据用户定义的代价函数，例如解释长度，优化词汇子集以满足两个主要特征。同时在嵌入空间中确保预测不变性，该方法通过内隐命中集和最大通用子集两种解法得出结果，并可以配置不同的扰动集来检测预测结果的偏见，提高 NLP 解释框架的效果。最后，文章在 SST、Twitter 和 IMDB 数据集上对三种常用情感分析任务进行了评估，并展示了该框架的有效性。