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        <title>Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models</title>
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        <namePart type="given">Alexander</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems. In this work, we study data-parallel training for the hierarchical Dirichlet process (HDP) topic model. Based upon a representation of certain conditional distributions within an HDP, we propose a doubly sparse data-parallel sampler for the HDP topic model. This sampler utilizes all available sources of sparsity found in natural language - an important way to make computation efficient. We benchmark our method on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under four days.</abstract>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
%A Terenin, Alexander
%A Magnusson, Måns
%A Jonsson, Leif
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F terenin-etal-2020-sparse
%X To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems. In this work, we study data-parallel training for the hierarchical Dirichlet process (HDP) topic model. Based upon a representation of certain conditional distributions within an HDP, we propose a doubly sparse data-parallel sampler for the HDP topic model. This sampler utilizes all available sources of sparsity found in natural language - an important way to make computation efficient. We benchmark our method on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under four days.
%R 10.18653/v1/2020.emnlp-main.234
%U https://aclanthology.org/2020.emnlp-main.234
%U https://doi.org/10.18653/v1/2020.emnlp-main.234
%P 2925-2934

Markdown (Informal)

[Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models](https://aclanthology.org/2020.emnlp-main.234) (Terenin et al., EMNLP 2020)

• Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models (Terenin et al., EMNLP 2020)

ACL

• Alexander Terenin, Måns Magnusson, and Leif Jonsson. 2020. Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2925–2934, Online. Association for Computational Linguistics.