Discrete Latent Variable Representations for Low-Resource Text Classification

ACL 2020 Shuning JinSam WisemanKarl StratosKaren Livescu

While much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically more space efficient. We consider several approaches to learning discrete latent variable models for text in the case where exact marginalization over these variables is intractable... (read more)

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