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        <title>A Joint Learning Approach for Semi-supervised Neural Topic Modeling</title>
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            <namePart type="given">Priyanka</namePart>
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    <abstract>Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of unsupervised neural topic models, which leverage deep generative models as opposed to traditional statistics-based topic models. We extend upon these neural topic models by introducing the Label-Indexed Neural Topic Model (LI-NTM), which is, to the extent of our knowledge, the first effective upstream semi-supervised neural topic model. We find that LI-NTM outperforms existing neural topic models in document reconstruction benchmarks, with the most notable results in low labeled data regimes and for data-sets with informative labels; furthermore, our jointly learned classifier outperforms baseline classifiers in ablation studies.</abstract>
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    <identifier type="doi">10.18653/v1/2022.spnlp-1.5</identifier>
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%0 Conference Proceedings
%T A Joint Learning Approach for Semi-supervised Neural Topic Modeling
%A Chiu, Jeffrey
%A Mittal, Rajat
%A Tumma, Neehal
%A Sharma, Abhishek
%A Doshi-Velez, Finale
%Y Vlachos, Andreas
%Y Agrawal, Priyanka
%Y Martins, André
%Y Lampouras, Gerasimos
%Y Lyu, Chunchuan
%S Proceedings of the Sixth Workshop on Structured Prediction for NLP
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F chiu-etal-2022-joint
%X Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of unsupervised neural topic models, which leverage deep generative models as opposed to traditional statistics-based topic models. We extend upon these neural topic models by introducing the Label-Indexed Neural Topic Model (LI-NTM), which is, to the extent of our knowledge, the first effective upstream semi-supervised neural topic model. We find that LI-NTM outperforms existing neural topic models in document reconstruction benchmarks, with the most notable results in low labeled data regimes and for data-sets with informative labels; furthermore, our jointly learned classifier outperforms baseline classifiers in ablation studies.
%R 10.18653/v1/2022.spnlp-1.5
%U https://aclanthology.org/2022.spnlp-1.5
%U https://doi.org/10.18653/v1/2022.spnlp-1.5
%P 40-51

Markdown (Informal)

[A Joint Learning Approach for Semi-supervised Neural Topic Modeling](https://aclanthology.org/2022.spnlp-1.5) (Chiu et al., spnlp 2022)

• A Joint Learning Approach for Semi-supervised Neural Topic Modeling (Chiu et al., spnlp 2022)

ACL

• Jeffrey Chiu, Rajat Mittal, Neehal Tumma, Abhishek Sharma, and Finale Doshi-Velez. 2022. A Joint Learning Approach for Semi-supervised Neural Topic Modeling. In Proceedings of the Sixth Workshop on Structured Prediction for NLP, pages 40–51, Dublin, Ireland. Association for Computational Linguistics.