Cross-Lingual Latent Topic Extraction. Duo Zhang, Qiaozhu Mei, ChengXiang Zhai. ACL 2010

• Key ideas
• Input: unaligned document sets in two languages, a bilingual dictionary
• Output: 
• a set of aligned topics (word distributions) in the two languages, that can characterize the shared topics
•  a topic coverage distribution for each language (coverage of each topic in that language)
• Method:
• Start with ML objective of PLSA
• Add a term to incorporate dictionary constraints (DC)
• Dictionary modeled as a weighted bipartite graph (weight = translation probability)
• ML using Generalized EM (because DC maximization has no closed-form solution)
• We can't maximize DC; instead just try to improve over current