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Autoregressive feedback is considered a necessity for successfulunconditional text generation using stochastic sequence models. However, suchfeedback is known to introduce systematic biases into the training process andit obscures a principle of generation: committing to global information andforgetting local nuances. We show that a non-autoregressive deep state spacemodel with a clear separation of global and local uncertainty can be built fromonly two ingredients: An independent noise source and a deterministictransition function. Recent advances on flow-based variational inference can beused to train an evidence lower-bound without resorting to annealing, auxiliarylosses or similar measures. The result is a highly interpretable generativemodel on par with comparable auto-regressive models on the task of wordgeneration.

Deep State Space Models for Unconditional Word Generation