Many tasks in NLP and IR require efficient document similarity computations. Beyond their common application to exploratory data analysis, latent variable topic models have been used to represent text in a low-dimensional space, independent of vocabulary, where documents may be compared. This paper focuses on the task of searching a large multilingual collection for pairs of documents that are translations of each other. We present (1) efficient, online inference for representing documents in several languages in a common topic space and (2) fast approximations for finding near neighbors in the probability simplex. Empirical evaluations show that these methods are as accurate as—and significantly faster than— Gibbs sampling and brute-force all-pairs search.
['Kriste Krstovski', 'David A. Smith']
Online Polylingual Topic Models for Fast Document Translation Detection