Memory-Augmented Generative Adversarial Transformers proposes incorporating external data through a memory bank alongside a Transformer model to enrich conversational AI.

• Model: Generative Adversarial Transformers
• Addition: Augmented memory bank
• Application: Goal-oriented dialogues, style adaptation
• Enhancements: Implementation of factual conditions
• Use cases: Conversational AI, style-constrained generation

This paper offers a novel perspective on how to integrate factual data into the language generation process, aiming to improve the efficiency and accuracy of conversational AI systems.