By illuminating latent structures in a corpus of text, topic models are an
essential tool for categorizing, summarizing, and exploring large collections
of documents. Probabilistic topic models, such as latent Dirichlet allocation
(LDA), describe how words in documents are generated via a set of latent
distributions called topics. Recently, the Embedded Topic Model (ETM) has
extended LDA to utilize the semantic information in word embeddings to derive
semantically richer topics. As LDA and its extensions are unsupervised models,
they aren't defined to make efficient use of a user's prior knowledge of the
domain. To this end, we propose the Keyword Assisted Embedded Topic Model
(KeyETM), which equips ETM with the ability to incorporate user knowledge in
the form of informative topic-level priors over the vocabulary. Using both
quantitative metrics and human responses on a topic intrusion task, we
demonstrate that KeyETM produces better topics than other guided, generative
models in the literature.

本文提出了关键词辅助嵌入式主题模型（KeyETM)，它使嵌入式主题模型（ETM）得以利用用户先前领域知识中的信息，从而在量化指标和主题干扰任务的实验中展现了优秀的表现，而不仅仅是将话题模型作为无监督模型。