Improving Adversarial Text Generation by Modeling the Distant Future

• URL: http://arxiv.org/abs/2005.01279v1
• Date: Mon, 4 May 2020 05:45:13 GMT
• Title: Improving Adversarial Text Generation by Modeling the Distant Future
• Authors: Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, Lawrence Carin
• Abstract summary: We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. We propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization.
• Score: 155.83051741029732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments demonstrate that the proposed method leads to improved performance.

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