  Topic models are valuable for understanding extensive document collections,
but they don't always identify the most relevant topics. Classical
probabilistic and anchor-based topic models offer interactive versions that
allow users to guide the models towards more pertinent topics. However, such
interactive features have been lacking in neural topic models. To correct this
lacuna, we introduce a user-friendly interaction for neural topic models. This
interaction permits users to assign a word label to a topic, leading to an
update in the topic model where the words in the topic become closely aligned
with the given label. Our approach encompasses two distinct kinds of neural
topic models. The first includes models where topic embeddings are trainable
and evolve during the training process. The second kind involves models where
topic embeddings are integrated post-training, offering a different approach to
topic refinement. To facilitate user interaction with these neural topic
models, we have developed an interactive interface. This interface enables
users to engage with and re-label topics as desired. We evaluate our method
through a human study, where users can relabel topics to find relevant
documents. Using our method, user labeling improves document rank scores,
helping to find more relevant documents to a given query when compared to no
user labeling.
