For example, if we want to predict whether a paper will be cited or not (Yogatama et al., 2011), the model can perform better if it knows beforehand the collections of words that represent certain themes (e.g., in ACL papers, these might include machine translation, parsing, etc.).
the model can focus on which topics will increase the probability of getting citations, and penalize weights for words in the same topic together, instead of treating each word separately.