While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated. In this work, we introduce a framework to quantify the value of explanations via the accuracy gains that they confer on a student model trained to simulate a teacher model. Crucially, the explanations are available to the student during training, but are not available at test time. Compared to prior proposals, our approach is less easily gamed, enabling principled, automatic, model-agnostic evaluation of attributions. Using our framework, we compare numerous attribution methods for text classification and question answering, and observe quantitative differences that are consistent (to a moderate to high degree) across different student model architectures and learning strategies.
Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble
Huawei Cloud（华为云）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）；Peking University（北京大学）；Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间安全学院，湖北省分布式系统安全重点实验室，湖北省大数据安全工程技术研究中心）
Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning
National Key Laboratory for Multimedia Information Processing, Peking University（北京大学多媒体信息处理国家重点实验室）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；Tencent Inc.（腾讯公司）；Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University（江苏省语言能力协同创新中心，江苏师范大学）
In Good GRACEs: Principled Teacher Selection for Knowledge Distillation
Princeton Language and Intelligence（普林斯顿语言与智能研究所）；Kempner Institute, Harvard（哈佛凯普纳研究所）；Microsoft Research, New York（微软研究院（纽约））；University of Pennsylvania（宾夕法尼亚大学）
When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection
University of Washington（华盛顿大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Southern California（南加州大学）；Independent Researcher（独立研究者）；National University of Singapore（新加坡国立大学）；Microsoft（微软）；Google（谷歌）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Northwestern University（西北大学）；Allen Institute for AI (AI2)（人工智能研究院（AI2））
