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arXiv · 1806.04550

Deep State Space Models for Unconditional Word Generation

Florian Schmidt·Thomas Hofmann
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Abstract

Autoregressive feedback is considered a necessity for successful unconditional text generation using stochastic sequence models. However, such feedback is known to introduce systematic biases into the training process and it obscures a principle of generation: committing to global information and forgetting local nuances. We show that a non-autoregressive deep state space model with a clear separation of global and local uncertainty can be built from only two ingredients: An independent noise source and a deterministic transition function. Recent advances on flow-based variational inference can be used to train an evidence lower-bound without resorting to annealing, auxiliary losses or similar measures. The result is a highly interpretable generative model on par with comparable auto-regressive models on the task of word generation.

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Florian Schmidt, Thomas Hofmann. 2018-06-12. Deep State Space Models for Unconditional Word Generation. https://arxiv.org/abs/1806.04550

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