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arxiv: 1205.2657 · v1 · submitted 2012-05-09 · 💻 cs.CL · cs.IR· cs.LG· stat.ML

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Multilingual Topic Models for Unaligned Text

Jordan Boyd-Graber , David Blei
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classification 💻 cs.CL cs.IRcs.LGstat.ML
keywords multilingualcorporamutotopicdocumentslanguagesmodeltext
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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.

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    LLM-XTM integrates LLM-guided topic refinement with self-consistency uncertainty quantification to improve coherence and alignment in cross-lingual topic models while reducing dependence on bilingual resources and rep...