Proceedings of the Sixth Workshop on Structured Prediction for NLP 2022
DOI: 10.18653/v1/2022.spnlp-1.5
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A Joint Learning Approach for Semi-supervised Neural Topic Modeling

Abstract: Topic models are some of the most popular ways to represent textual data in an interpretable 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… Show more

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