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What is: Contextualized Topic Models?

SourceCross-lingual Contextualized Topic Models with Zero-shot Learning
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

Contextualized Topic Models are based on the Neural-ProdLDA variational autoencoding approach by Srivastava and Sutton (2017).

This approach trains an encoding neural network to map pre-trained contextualized word embeddings (e.g., BERT) to latent representations. Those latent representations are sampled variationally from a Gaussian distribution N(μ,σ2)N(\mu, \sigma^2) and passed to a decoder network that has to reconstruct the document bag-of-word representation.