To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs

Thomas Scialom, Paul-Alexis Dray, Jacopo Staiano, sylvain lamprier, Benjamin Piwowarski

Keywords: NLP - NLG - Language - Summarization - GAN - Generative - Adversarial - Discriminator - Cooperative - Decoding - Search - Beam

TL;DR: We propose a new training framework for Language GANs based on cooperative decoding search and self-training.

Abstract:

Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods. This is a much harder setting than for continuous tasks, which enjoy gradient flows from discriminators to generators, usually leading to dramatic learning instabilities. However, we claim that this can be solved by making discriminator and generator networks cooperate to produce output sequences during training.
These cooperative outputs, inherently built to obtain higher discrimination scores, not only provide denser rewards for training but also form a more compact artificial set for discriminator training, hence improving its accuracy and stability.
In this paper, we show that our SelfGAN framework, built on this cooperative principle, outperforms Teacher Forcing and obtains state-of-the-art results on two challenging tasks, Summarization and Question Generation.

Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.