Endowing chatbots with a consistent persona is essential to an engaging
conversation, yet it remains an unresolved challenge. In this work, we propose
a new retrieval-enhanced approach for personalized response generation.
Specifically, we design a hierarchical transformer retriever trained on
dialogue domain data to perform personalized retrieval and a context-aware
prefix encoder that fuses the retrieved information to the decoder more
effectively. Extensive experiments on a real-world dataset demonstrate the
effectiveness of our model at generating more fluent and personalized
responses. We quantitatively evaluate our model's performance under a suite of
human and automatic metrics and find it to be superior compared to
state-of-the-art baselines on English Reddit conversations.

本研究提出了一种基于检索增强的个性化响应生成方法，通过在对话领域数据上设计分层的 Transformer 检索器和上下文感知前缀编码器，将检索到的信息更有效地融合到解码器中，从而在英语 Reddit 对话中生成更流畅和个性化的响应，与现有技术相比性能更优。