Papers with generative text classifiers

1 papers
Latent-Variable Generative Models for Data-Efficient Text Classification (D19-1)

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Challenge: Generative classifiers offer potential advantages over discriminative classifications, including data efficiency and zero-shot learning.
Approach: They introduce discrete latent variables into generative story to improve classifiers' performance . they empirically characterize performance of their models on six text classification datasets .
Outcome: The proposed model outperforms discriminative and generative classifiers on six text classification datasets.

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