Polylingual topic models
David Mimnoa b, Hanna Wallacha b, Jason Naradowskya b, David A. Smitha b, Andrew McCalluma b
- aUniversity of Massachusetts, Amherst, MA
- bUniversity of Massachusetts, Amherst MA
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Abstract
Topic models are a useful tool for analyzing large text collections, but have previously been applied in only monolingual, or at most bilingual, contexts. Meanwhile, massive collections of interlinked documents in dozens of languages, such as Wikipedia, are now widely available, calling for tools that can characterize content in many languages. We introduce a polylingual topic model that discovers topics aligned across multiple languages. We explore the model's characteristics using two large corpora, each with over ten different languages, and demonstrate its usefulness in supporting machine translation and tracking topic trends across languages.
TL;DRThis work introduces a polylingual topic model that discovers topics aligned across multiple languages and demonstrates its usefulness in supporting machine translation and tracking topic trends across languages. (Semantic Scholar)