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        <title>Auto-Encoding Variational Bayes for Inferring Topics and Visualization</title>
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    <abstract>Visualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from text, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative model to jointly find topics and visualization, allowing the semantics to be infused in the visualization space for a meaningful interpretation. A major challenge that prevents these methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black box, it can handle model changes efficiently with little mathematical rederivation effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines.</abstract>
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%0 Conference Proceedings
%T Auto-Encoding Variational Bayes for Inferring Topics and Visualization
%A Pham, Dang
%A Le, Tuan
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F pham-le-2020-auto
%X Visualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from text, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative model to jointly find topics and visualization, allowing the semantics to be infused in the visualization space for a meaningful interpretation. A major challenge that prevents these methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black box, it can handle model changes efficiently with little mathematical rederivation effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines.
%R 10.18653/v1/2020.coling-main.458
%U https://aclanthology.org/2020.coling-main.458
%U https://doi.org/10.18653/v1/2020.coling-main.458
%P 5223-5234

Markdown (Informal)

[Auto-Encoding Variational Bayes for Inferring Topics and Visualization](https://aclanthology.org/2020.coling-main.458) (Pham & Le, COLING 2020)

• Auto-Encoding Variational Bayes for Inferring Topics and Visualization (Pham & Le, COLING 2020)

ACL

• Dang Pham and Tuan Le. 2020. Auto-Encoding Variational Bayes for Inferring Topics and Visualization. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5223–5234, Barcelona, Spain (Online). International Committee on Computational Linguistics.