Understanding Crosslingual Transfer Mechanisms in Probabilistic Topic Modeling
Probabilistic topic modeling is a popular choice as the first step of crosslingual tasks to enable knowledge transfer and extract multilingual features. While many multilingual topic models have been developed, their assumptions on the training corpus are quite varied, and it is not clear how well the models can be applied under various training conditions. In this paper, we systematically study the knowledge transfer mechanisms behind different multilingual topic models, and through a broad set of experiments with four models on ten languages, we provide empirical insights that can inform the selection and future development of multilingual topic models.
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Shudong Hao (edit)
Michael J. Paul (edit)
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10/15/18 07:08PM
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arxivml: "Understanding Crosslingual Transfer Mechanisms in Probabilistic Topic Modeling", Shudong Hao, Michael J. Paul https://t.co/0lLBBBRzi7
arxiv_cscl: Understanding Crosslingual Transfer Mechanisms in Probabilistic Topic Modeling https://t.co/KmWYBrruPX
ComputerPapers: Understanding Crosslingual Transfer Mechanisms in Probabilistic Topic Modeling. https://t.co/w5eoKx602U
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