论文标题
基于实例的跨度表示：通过指定实体识别的案例研究
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
论文作者
论文摘要
模型预测的可解释理由在实际应用中起着至关重要的作用。在这项研究中，我们开发了具有结构化预测的可解释推理过程的模型。具体来说，我们提出了一种基于实例的学习方法，该方法可以学习跨度之间的相似性。在推理时，每个跨度都根据训练集中的类似跨度分配了一个类标签，在该标签中很容易理解每个训练实例对预测的贡献。通过对命名实体识别的经验分析，我们证明了我们的方法可以构建具有高解释性的模型而无需牺牲绩效。
Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, each span is assigned a class label based on its similar spans in the training set, where it is easy to understand how much each training instance contributes to the predictions. Through empirical analysis on named entity recognition, we demonstrate that our method enables to build models that have high interpretability without sacrificing performance.
