Contextualized Topic Models

Introduced by Bianchi et al. in Cross-lingual Contextualized Topic Models with Zero-shot Learning

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 $N(\mu, \sigma^2)$ and passed to a decoder network that has to reconstruct the document bag-of-word representation.

Source: Cross-lingual Contextualized Topic Models with Zero-shot Learning

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Topic Models	7	33.33%
Bayesian Optimization	2	9.52%
Clustering	1	4.76%
Document Embedding	1	4.76%
Document Classification	1	4.76%
Bayesian Optimisation	1	4.76%
Classification	1	4.76%
Cross-Lingual Transfer	1	4.76%
General Classification	1	4.76%