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Found 3 papers, 2 papers with code
Distributional Discrepancy: A Metric for Unconditional Text Generation
The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data.
Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation
To alleviate the exposure bias, generative adversarial networks (GAN) use the discriminator to update the generator's parameters directly, but they fail by being evaluated precisely.
The Detection of Distributional Discrepancy for Text Generation
In this paper, we theoretically propose two metric functions to measure the distributional difference between real text and generated text.
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