论文标题
排除和包容性 - 一种模型的不可知论方法，可在DNN中具有重要意义
Exclusion and Inclusion -- A model agnostic approach to feature importance in DNNs
论文作者
论文摘要
NLP中的深层神经网络使系统能够学习复杂的非线性关系。能够将DNN用于现实世界应用的主要瓶颈之一是它们作为黑匣子的特征。为了解决此问题，我们引入了一种模型不可知算法，该算法计算出输入特征的短语重要性。我们认为，通过进行回归和分类实验，我们的方法可以推广到各种任务。我们还观察到我们的方法对离群值是强大的，这意味着它只捕获了意见的基本方面。
Deep Neural Networks in NLP have enabled systems to learn complex non-linear relationships. One of the major bottlenecks towards being able to use DNNs for real world applications is their characterization as black boxes. To solve this problem, we introduce a model agnostic algorithm which calculates phrase-wise importance of input features. We contend that our method is generalizable to a diverse set of tasks, by carrying out experiments for both Regression and Classification. We also observe that our approach is robust to outliers, implying that it only captures the essential aspects of the input.
