@InProceedings{bhatia-lau-baldwin:2017:CoNLL,
  author    = {Bhatia, Shraey  and  Lau, Jey Han  and  Baldwin, Timothy},
  title     = {An Automatic Approach for Document-level Topic Model Evaluation},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://aclweb.org/anthology/K17-1022}
}