TrueLabel +混淆：分析多重评级的概率模型谱
TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing Multiple Ratings
摘要：
本文重新审视了由不同的法官给出的多个评级的分析问题。不同于以往的工作，侧重于从嘈杂的众包评级中提取真正的标签，我们强调获得诊断性的见解，我们的内部训练有素的法官。我们将著名的DawidSkene模型（Dawid & Skene，1979）推广到相同的“TrueLabel + Confusion”范式下的概率模型谱，并表明我们提出的分层贝叶斯模型（称为HybridConfusion）在合成和真实世界的数据集上的性能始终优于DawidSkene。
英文摘要：
This paper revisits the problem of analyzing multiple ratings given by different judges. Different from previous work that focuses on distilling the true labels from noisy crowdsourcing ratings, we emphasize gaining diagnostic insights into our in-house well-trained judges. We generalize the well-known DawidSkene model (Dawid & Skene, 1979) to a spectrum of probabilistic models under the same "TrueLabel + Confusion" paradigm, and show that our proposed hierarchical Bayesian model, called HybridConfusion, consistently outperforms DawidSkene on both synthetic and real-world data sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
