Dynamic topic models track the evolution of topics in sequential documents,
which have derived various applications like trend analysis and opinion mining.
However, existing models suffer from repetitive topic and unassociated topic
issues, failing to reveal the evolution and hindering further applications. To
address these issues, we break the tradition of simply chaining topics in
existing work and propose a novel neural \modelfullname. We introduce a new
evolution-tracking contrastive learning method that builds the similarity
relations among dynamic topics. This not only tracks topic evolution but also
maintains topic diversity, mitigating the repetitive topic issue. To avoid
unassociated topics, we further present an unassociated word exclusion method
that consistently excludes unassociated words from discovered topics. Extensive
experiments demonstrate our model significantly outperforms state-of-the-art
baselines, tracking topic evolution with high-quality topics, showing better
performance on downstream tasks, and remaining robust to the hyperparameter for
evolution intensities. Our code is available at this https URL .

提出了一种新的神经模型来解决现有动态主题模型中的重复主题和不相关主题问题，并通过引入新的进化跟踪对比学习方法和不相关词排除方法来提高主题进化的质量和多样性。