Generative and Discriminative Text Classification with Recurrent Neural Networks

6 Mar 2017Dani YogatamaChris DyerWang LingPhil Blunsom

We empirically characterize the performance of discriminative and generative LSTM models for text classification. We find that although RNN-based generative models are more powerful than their bag-of-words ancestors (e.g., they account for conditional dependencies across words in a document), they have higher asymptotic error rates than discriminatively trained RNN models... (read more)

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