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Creators/Authors contains: "Tumma, Neehal"

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  1. ; ; ; ; (, Proceedings of the Sixth Workshop on Structured Prediction for NLP)
    Topic models are some of the most popular ways to represent textual data in an interpret- able manner. Recently, advances in deep gen- erative models, specifically auto-encoding vari- ational Bayes (AEVB), have led to the intro- duction of unsupervised neural topic models, which leverage deep generative models as op- posed to traditional statistics-based topic mod- els. 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 la- bels; furthermore, our jointly learned classi- fier outperforms baseline classifiers in ablation studies. 
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