Task-oriented dialogue systems are either modularized with separate dialogue
state tracking (DST) and management steps or end-to-end trainable. In either
case, the knowledge base (KB) plays an essential role in fulfilling user
requests. Modularized systems rely on DST to interact with the KB, which is
expensive in terms of annotation and inference time. End-to-end systems use the
KB directly as input, but they cannot scale when the KB is larger than a few
hundred entries. In this paper, we propose a method to embed the KB, of any
size, directly into the model parameters. The resulting model does not require
any DST or template responses, nor the KB as input, and it can dynamically
update its KB via fine-tuning. We evaluate our solution in five task-oriented
dialogue datasets with small, medium, and large KB size. Our experiments show
that end-to-end models can effectively embed knowledge bases in their
parameters and achieve competitive performance in all evaluated datasets.

本文提出了一种将知识库（KB）嵌入到模型参数中的方法，从而实现无需在交互中使用 DST 或模板响应以及 KB 作为输入的端到端对话系统；实验证明，基于该方法的端到端模型在各种 KB 大小下能够实现优异的性能。