论文标题
影响序列标记模型的功能
Influence Functions for Sequence Tagging Models
论文作者
论文摘要
许多语言任务（例如，命名的实体识别，言论部分标记和语义角色标签）自然被构架为序列标记问题。但是，对于序列标记模型的可解释性方法的工作相对较少。在本文中，我们扩展了影响功能（旨在将预测追溯到告知它们的训练点）以对任务进行序列标记。我们将训练实例段的影响定义为扰动该段中标签对测试段级别预测的影响。我们提供了有效的近似来计算这一点，并表明它以真实的细分影响跟踪，以经验测量。我们通过使用该方法来识别两个命名实体识别语料库中的系统注释错误来显示细分影响的实际实用性。可以在https://github.com/successar/segrend_influence_functions上获得复制我们的结果的代码。
Many language tasks (e.g., Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling) are naturally framed as sequence tagging problems. However, there has been comparatively little work on interpretability methods for sequence tagging models. In this paper, we extend influence functions - which aim to trace predictions back to the training points that informed them - to sequence tagging tasks. We define the influence of a training instance segment as the effect that perturbing the labels within this segment has on a test segment level prediction. We provide an efficient approximation to compute this, and show that it tracks with the true segment influence, measured empirically. We show the practical utility of segment influence by using the method to identify systematic annotation errors in two named entity recognition corpora. Code to reproduce our results is available at https://github.com/successar/Segment_Influence_Functions.
