@conference {12408,
	title = {Multilingual topic models for unaligned text},
	booktitle = {Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence},
	series = {UAI {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {75 - 82},
	publisher = {AUAI Press},
	organization = {AUAI Press},
	address = {Arlington, Virginia, United States},
	abstract = {We develop the multilingual topic model for unaligned text (MuTo), a probabilistic model of text that is designed to analyze corpora composed of documents in two languages. From these documents, MuTo uses stochastic EM to simultaneously discover both a matching between the languages and multilingual latent topics. We demonstrate that MuTo is able to find shared topics on real-world multilingual corpora, successfully pairing related documents across languages. MuTo provides a new framework for creating multilingual topic models without needing carefully curated parallel corpora and allows applications built using the topic model formalism to be applied to a much wider class of corpora.},
	isbn = {978-0-9749039-5-8},
	url = {http://dl.acm.org/citation.cfm?id=1795114.1795124},
	author = {Jordan Boyd-Graber and Blei,David M.}
}
