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Improving Adversarial Text Generation by Modeling the Distant Future
[article]
2020
arXiv
pre-print
Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a
arXiv:2005.01279v1
fatcat:txiqigbiqfe57k5mednpv25qvi