Improving Adversarial Text Generation by Modeling the Distant Future
arXiv:2005.01279
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.
ACL 2020. arXiv admin note: substantial text overlap with arXiv:1811.00696
References in corpus (6)
- An Actor-Critic Algorithm for Sequence Prediction
- GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
- Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
- Long Text Generation via Adversarial Training with Leaked Information
- Model-based Adversarial Imitation Learning
- Improving Sequence-to-Sequence Learning via Optimal Transport