Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data. However, there is no standard and effective metric to evaluate the quality of multilingual topics. We introduce a new intrinsic evaluation of multilingual topic models that correlates well with human judgments of multilingual topic coherence as well as performance in downstream applications. Importantly, we also study evaluation for low-resource languages. Because standard metrics fail to accurately measure topic quality when robust external resources are unavailable, we propose an adaptation model that improves the accuracy and reliability of these metrics in low-resource settings.

翻译：多语文专题模型能够通过连贯的多语种数据摘要进行不同语文的文件分析,然而,没有标准和有效的衡量标准来评价多语种专题的质量。我们引入了对多语种专题模型的新的内在评价,这种评价与人类对多语种专题一致性的判断以及下游应用的绩效密切相关。重要的是,我们还研究低资源语言的评价。由于标准指标在缺乏强有力的外部资源时无法准确衡量专题质量,我们提出了一个适应模型,以提高这些指标在低资源环境中的准确性和可靠性。