  A recent line of work in natural language processing has aimed to combine
language models and topic models. These topic-guided language models augment
neural language models with topic models, unsupervised learning methods that
can discover document-level patterns of word use. This paper compares the
effectiveness of these methods in a standardized setting. We study four
topic-guided language models and two baselines, evaluating the held-out
predictive performance of each model on four corpora. Surprisingly, we find
that none of these methods outperform a standard LSTM language model baseline,
and most fail to learn good topics. Further, we train a probe of the neural
language model that shows that the baseline's hidden states already encode
topic information. We make public all code used for this study.
