@inproceedings{bhatia-etal-2017-automatic,
    title = "An Automatic Approach for Document-level Topic Model Evaluation",
    author = "Bhatia, Shraey  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    editor = "Levy, Roger  and
      Specia, Lucia",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/K17-1022/",
    doi = "10.18653/v1/K17-1022",
    pages = "206--215",
    abstract = "Topic models jointly learn topics and document-level topic distribution. Extrinsic evaluation of topic models tends to focus exclusively on topic-level evaluation, e.g. by assessing the coherence of topics. We demonstrate that there can be large discrepancies between topic- and document-level model quality, and that basing model evaluation on topic-level analysis can be highly misleading. We propose a method for automatically predicting topic model quality based on analysis of document-level topic allocations, and provide empirical evidence for its robustness."
}