Probabilistic topic modeling is a common first step in crosslingual tasks to enable knowledge transfer and extract multilingual features. While many multilingual topic models have been developed, their assumptions about the training corpus are quite varied, and it is not clear how well the different models can be utilized under various training conditions. In this article, the knowledge transfer mechanisms behind different multilingual topic models are systematically studied, and through a broad set of experiments with four models on ten languages, we provide empirical insights that can inform the selection and future development of multilingual topic models.

中文翻译：

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概率主题模型中跨语言迁移的实证研究

概率主题建模是跨语言任务中常见的第一步，以实现知识转移和提取多语言特征。虽然已经开发了许多多语言主题模型，但它们对训练语料库的假设却各不相同，并且不清楚不同模型在各种训练条件下的使用情况。在本文中，系统研究了不同多语言主题模型背后的知识转移机制，并通过对 10 种语言的四种模型的广泛实验，提供了可以为多语言主题模型的选择和未来发展提供信息的实证见解。