For topic models, such as LDA, that use a bag-of-words assumption, it becomes especially important to break the corpus into appropriately-sized "documents". Since the models are estimated solely from the term cooccurrences, extensive documents such as books or long journal articles lead to diffuse statistics, and short documents such as forum posts or product reviews can lead to sparsity. This paper describes practical inference procedures for hierarchical models that smooth topic estimates for smaller sections with hyperpriors over larger documents. Importantly for large collections, these online variational Bayes inference methods perform a single pass over a corpus and achieve better perplexity than "flat" topic models on monolingual and multilingual data. Furthermore, on the task of detecting document translation pairs in large multilingual collections, polylingual topic models (PLTM) with multi-level hyperpriors (mlhPLTM) achieve significantly better performance than existing online PLTM models while retaining computational efficiency.
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