Conversational AI systems that rely on Large Language Models, like Transformers, have difficulty interweaving external data (like facts) with the language they generate. Vanilla Transformer architectures are not designed for answering factual questions with high accuracy. This paper investigates a possible route for addressing this problem. We propose to extend the standard Transformer architecture with an additional memory bank holding extra information (such as facts drawn from a knowledge base), and an extra attention layer for addressing this memory. We add this augmented memory to a Generative Adversarial Network-inspired Transformer architecture. This setup allows for implementing arbitrary felicity conditions on the generated language of the Transformer. We first demonstrate how this machinery can be deployed for handling factual questions in goal-oriented dialogues. Secondly, we demonstrate that our approach can be useful for applications like {\it style adaptation} as well: the adaptation of utterances according to certain stylistic (external) constraints, like social properties of human interlocutors in dialogues.

翻译：依赖大型语言模型（如Transformer）的对话式AI系统在将外部数据（如事实）与生成的语言相结合时存在困难。普通Transformer架构并不适用于高精度回答事实性问题。本文探讨了解决该问题的可能途径。我们提出在标准Transformer架构中扩展一个额外的记忆库（用于存储诸如从知识库中提取的事实等额外信息）以及一个用于访问该记忆的额外注意力层。我们将这种增强型记忆融入到受生成对抗网络启发的Transformer架构中。该框架允许对Transformer生成的任意语言施加约束条件。我们首先展示了该机制如何应用于处理目标导向对话中的事实性问题。其次，我们证明了该方法同样适用于诸如**风格适应**等应用场景——根据特定的外部约束条件（如对话中人类对话者的社会属性）调整话语风格。