There is a lack of quantitative measures to evaluate the progression of
topics through time in dynamic topic models (DTMs). Filling this gap, we
propose a novel evaluation measure for DTMs that analyzes the changes in the
quality of each topic over time. Additionally, we propose an extension
combining topic quality with the model's temporal consistency. We demonstrate
the utility of the proposed measure by applying it to synthetic data and data
from existing DTMs. We also conducted a human evaluation, which indicates that
the proposed measure correlates well with human judgment. Our findings may help
in identifying changing topics, evaluating different DTMs, and guiding future
research in this area.

本研究提出了一种用于动态主题模型中定量评估主题演变的新型评估方法，分析了每个主题在时间上的质量变化，并将主题质量与模型的时间一致性相结合。通过对合成数据和现有动态主题模型数据的应用以及人工评估，验证了所提出的评估方法的实用性。研究结果可用于识别变化的主题，评估不同的动态主题模型，并指导未来研究。

评估动态主题模型

Evaluating Dynamic Topic Models

Topic models are often used to identify human-interpretable topics to help
make sense of large document collections. We use knowledge distillation to
combine the best attributes of probabilistic topic models and pretrained
transformers. Our modular method can be straightforwardly applied with any
neural topic model to improve topic quality, which we demonstrate using two
models having disparate architectures, obtaining state-of-the-art topic
coherence. We show that our adaptable framework not only improves performance
in the aggregate over all estimated topics, as is commonly reported, but also
in head-to-head comparisons of aligned topics.

使用知识蒸馏将概率主题模型和预训练转换器的最佳属性结合起来，以提高主题质量和性能。