TY - JOUR AU - Wu, Qingyang AU - Li, Lei AU - Yu, Zhou PY - 2021/05/18 Y2 - 2024/03/28 TI - TextGAIL: Generative Adversarial Imitation Learning for Text Generation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 16 SE - AAAI Technical Track on Speech and Natural Language Processing III DO - 10.1609/aaai.v35i16.17656 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17656 SP - 14067-14075 AB - Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generative adversarial imitation learning framework for text generation that uses large pre-trained language models to provide more reliable reward guidance. As previous text GANs suffer from high variance of gradients, we apply contrastive discriminator, and proximal policy optimization (PPO) to stabilize and improve text generation performance. For evaluation, we conduct experiments on a diverse set of unconditional and conditional text generation tasks. Experimental results show that TextGAIL achieves better performance in terms of both quality and diversity than the MLE baseline. We also validate our intuition that TextGAIL's discriminator demonstrates the capability of providing reasonable rewards with an additional task. ER -