结合特征和实例属性检测伪影
Combining Feature and Instance Attribution to Detect Artifacts
摘要：
训练主导NLP的深度神经网络需要大型数据集。这些通常是自动收集或通过众包，并可能表现出系统性的偏见或注释工件。后者指的是输入和输出之间的虚假相关性，这些相关性并不代表特征和类之间普遍持有的因果关系;利用这种相关性的模型可能看起来很好地执行了给定的任务，但在样本数据之外失败了。在本文中，我们评估使用不同的属性方法来帮助识别的训练数据文物。我们提出了新的混合方法，结合显着性图（突出重要的输入功能）与实例归因方法（检索训练样本影响到一个给定的预测）。我们表明，这个建议的训练特征属性可以用来有效地发现工件时，一个具有挑战性的验证集是可用的训练数据。我们还进行了一个小型的用户研究，以评估这些方法是否在实践中对NLP研究人员有用，并取得了可喜的成果。我们为本文中的所有方法和实验提供代码。
英文摘要：
Training the deep neural networks that dominate NLP requires large datasets. These are often collected automatically or via crowdsourcing, and may exhibit systematic biases or annotation artifacts. By the latter we mean spurious correlations between inputs and outputs that do not represent a generally held causal relationship between features and classes; models that exploit such correlations may appear to perform a given task well, but fail on out of sample data. In this paper we evaluate use of different attribution methods for aiding identification of training data artifacts. We propose new hybrid approaches that combine saliency maps (which highlight important input features) with instance attribution methods (which retrieve training samples influential to a given prediction). We show that this proposed training-feature attribution can be used to efficiently uncover artifacts in training data when a challenging validation set is available. We also carry out a small user study to evaluate whether these methods are useful to NLP researchers in practice, with promising results. We make code for all methods and experiments in this paper available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
