论文标题
寻找有影响力的实例，以进行远处监督的关系提取
Finding Influential Instances for Distantly Supervised Relation Extraction
论文作者
论文摘要
远处监督（DS）是扩展数据集以增强关系提取（RE）模型的强大方法，但通常遭受高标签噪声的痛苦。基于注意力，增强学习或GAN的当前作品是黑盒模型，因此它们既不提供对DS中样本选择的有意义的解释，也不提供不同领域的稳定性。相反，这项工作提出了一种新型的模型无形的实例采样方法，用于通过影响函数（如果），即reif。我们的方法根据if IF中标识了袋中有利/不利的实例，然后进行动态实例采样。我们设计了一种快速影响采样算法，该算法将计算复杂性从$ \ Mathcal {O}（Mn）$降低到$ \ Mathcal {O}（1）$，并分析了其在所选采样功能上的鲁棒性。实验表明，通过简单地对培训期间的有利实例进行采样，Reif可以赢得一系列具有复杂体系结构的基线。我们还证明了REIF可以支持可解释的实例选择。
Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise. Current works based on attention, reinforcement learning, or GAN are black-box models so they neither provide meaningful interpretation of sample selection in DS nor stability on different domains. On the contrary, this work proposes a novel model-agnostic instance sampling method for DS by influence function (IF), namely REIF. Our method identifies favorable/unfavorable instances in the bag based on IF, then does dynamic instance sampling. We design a fast influence sampling algorithm that reduces the computational complexity from $\mathcal{O}(mn)$ to $\mathcal{O}(1)$, with analyzing its robustness on the selected sampling function. Experiments show that by simply sampling the favorable instances during training, REIF is able to win over a series of baselines that have complicated architectures. We also demonstrate that REIF can support interpretable instance selection.
