As the size of the datasets getting larger, accurately annotating such datasets is becoming more impractical due to the expensiveness on both time and economy. Therefore, crowd-sourcing has been widely adopted to alleviate the cost of collecting labels, which also inevitably introduces label noise and eventually degrades the performance of the model. To learn from crowd-sourcing annotations, modeling the expertise of each annotator is a common but challenging paradigm, because the annotations collected by crowd-sourcing are usually highly-sparse. To alleviate this problem, we propose Coupled Confusion Correction (CCC), where two models are simultaneously trained to correct the confusion matrices learned by each other. Via bi-level optimization, the confusion matrices learned by one model can be corrected by the distilled data from the other. Moreover, we cluster the ``annotator groups'' who share similar expertise so that their confusion matrices could be corrected together. In this way, the expertise of the annotators, especially of those who provide seldom labels, could be better captured. Remarkably, we point out that the annotation sparsity not only means the average number of labels is low, but also there are always some annotators who provide very few labels, which is neglected by previous works when constructing synthetic crowd-sourcing annotations. Based on that, we propose to use Beta distribution to control the generation of the crowd-sourcing labels so that the synthetic annotations could be more consistent with the real-world ones. Extensive experiments are conducted on two types of synthetic datasets and three real-world datasets, the results of which demonstrate that CCC significantly outperforms state-of-the-art approaches.

翻译：随着数据集规模不断增大，由于时间和经济成本过高，精确标注这些数据集变得越来越不切实际。因此，众包被广泛采用以降低收集标签的成本，但这也不可避免地引入了标签噪声，最终降低了模型的性能。为了从众包标注中学习，建模每个标注者的专业水平是一种常见但具有挑战性的范式，因为通过众包收集的标注通常高度稀疏。为了缓解这一问题，我们提出了耦合混淆修正（CCC），该方法同时训练两个模型，以纠正彼此学习到的混淆矩阵。通过双层优化，一个模型学习到的混淆矩阵可以被另一个模型蒸馏得到的数据进行修正。此外，我们对具有相似专业水平的“标注者组”进行聚类，以便能够共同修正它们的混淆矩阵。通过这种方式，可以更好地捕捉标注者（尤其是那些提供较少标签的标注者）的专业水平。值得注意的是，我们指出标注稀疏性不仅意味着标签的平均数量低，还意味着总有一些标注者只提供极少数标签，这一点在以往构建合成众包标注时被忽略了。基于此，我们提出使用Beta分布来控制众包标签的生成，从而使合成标注与真实世界的标注更加一致。在两类合成数据集和三个真实世界数据集上进行了大量实验，结果表明CCC显著优于现有最先进的方法。