Generative Cooperative Networks for Natural Language Generation

• URL: http://arxiv.org/abs/2201.12320v1
• Date: Fri, 28 Jan 2022 18:36:57 GMT
• Title: Generative Cooperative Networks for Natural Language Generation
• Authors: Sylvain Lamprier and Thomas Scialom and Antoine Chaffin and Vincent Claveau and Ewa Kijak and Jacopo Staiano and Benjamin Piwowarski
• Abstract summary: We introduce Generative Cooperative Networks, in which the discriminator architecture is cooperatively used along with the generation policy to output samples of realistic texts. We give theoretical guarantees of convergence for our approach, and study various efficient decoding schemes to empirically achieve state-of-the-art results in two main NLG tasks.
• Score: 25.090455367573988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation. However, for discrete outputs such as language, optimizing GANs remains an open problem with many instabilities, as no gradient can be properly back-propagated from the discriminator output to the generator parameters. An alternative is to learn the generator network via reinforcement learning, using the discriminator signal as a reward, but such a technique suffers from moving rewards and vanishing gradient problems. Finally, it often falls short compared to direct maximum-likelihood approaches. In this paper, we introduce Generative Cooperative Networks, in which the discriminator architecture is cooperatively used along with the generation policy to output samples of realistic texts for the task at hand. We give theoretical guarantees of convergence for our approach, and study various efficient decoding schemes to empirically achieve state-of-the-art results in two main NLG tasks.

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