TextGAIL: Generative Adversarial Imitation Learning for Text Generation

By Qingyang Wu, Lei Li, Zhou Yu. 2020

57 citations recorded by the source corpus.

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.

Subfields: Natural Language Processing, Generative Models

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Citations in the corpus

Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization (2022, methodology): “…et al., 2019), SS for Transformers (Mihaylova & Martins, 2019), Diffential SS (Goyal et al., 2017), LOLS (Lampouras & Vlachos, 2016; Chang et al., 2015), TextGAIL (Wu et al., 2021b), and SEARNN (Leblond et al., 2017), have been inspired by DAGGER (Ross et al., 2011) and SEARN (Daumé et al., 2009).”