Multilingual topic models enable crosslingual tasks by extracting consistent
topics from multilingual corpora. Most models require parallel or comparable
training corpora, which limits their ability to generalize. In this paper, we
first demystify the knowledge transfer mechanism behind multilingual topic
models by defining an alternative but equivalent formulation. Based on this
analysis, we then relax the assumption of training data required by most
existing models, creating a model that only requires a dictionary for training.
Experiments show that our new method effectively learns coherent multilingual
topics from partially and fully incomparable corpora with limited amounts of
dictionary resources.

本文提出了一种只需字典作为训练数据的多语言主题模型，并在部分和完全不可比的语料库中有效地学习到一致的多语言主题，从而实现跨语言任务。

从不同语料库中学习多语言话题

Learning Multilingual Topics from Incomparable Corpus

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.

介绍了一种评估多语言主题模型质量的方法，该方法考虑到外部资源不足的情况，并提出了一种适应性模型来提高指标的准确性和可靠性。