Evaluating Text GANs as Language Models

NAACL 2019 Guy TevetGavriel HabibVered ShwartzJonathan Berant

Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of ``exposure bias''. However, A major hurdle for understanding the potential of GANs for text generation is the lack of a clear evaluation metric... (read more)

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