The growing need to analyze large collections of documents has led to great
developments in topic modeling. Since documents are frequently associated with
other related variables, such as labels or ratings, much interest has been
placed on supervised topic models. However, the nature of most annotation
tasks, prone to ambiguity and noise, often with high volumes of documents, deem
learning under a single-annotator assumption unrealistic or unpractical for
most real-world applications. In this article, we propose two supervised topic
models, one for classification and another for regression problems, which
account for the heterogeneity and biases among different annotators that are
encountered in practice when learning from crowds. We develop an efficient
stochastic variational inference algorithm that is able to scale to very large
datasets, and we empirically demonstrate the advantages of the proposed model
over state-of-the-art approaches.

本文提出了两种基于监督学习的主题模型，分别用于分类和回归，能够考虑到实践中在不同的标注者之间遇到的多样性和偏见。同时，我们还开发了一种有效的随机变分推断算法，能够适应非常大的数据集，并在实验中证明了该模型相对于现有技术的优越性。