论文标题
Prototex：用原型张量解释模型决策
ProtoTEx: Explaining Model Decisions with Prototype Tensors
论文作者
论文摘要
我们提出了Prototex，这是一种基于原型网络的新型白盒NLP分类体系结构。 Prototex忠实地解释了基于原型张量的模型决策，该原型张量编码训练示例的潜在簇。在推论时，分类决策基于输入文本和原型张量之间的距离，这是通过与最具影响力原型最相似的训练示例解释的。我们还描述了一种新型的交织训练算法，该算法有效地处理了以缺乏指示特征为特征的类。在一项宣传检测任务中，Prototex精度与Bart-large相匹配，并超过了Bert-large，并提供了提供忠实解释的额外好处。一项用户研究还表明，基于原型的解释有助于非专家在线新闻中更好地识别宣传。
We present ProtoTEx, a novel white-box NLP classification architecture based on prototype networks. ProtoTEx faithfully explains model decisions based on prototype tensors that encode latent clusters of training examples. At inference time, classification decisions are based on the distances between the input text and the prototype tensors, explained via the training examples most similar to the most influential prototypes. We also describe a novel interleaved training algorithm that effectively handles classes characterized by the absence of indicative features. On a propaganda detection task, ProtoTEx accuracy matches BART-large and exceeds BERT-large with the added benefit of providing faithful explanations. A user study also shows that prototype-based explanations help non-experts to better recognize propaganda in online news.
