  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.
