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.

翻译：赋予聊天机器人一致的角色个性对于引人入胜的对话至关重要，但这仍是一项未解决的挑战。在本工作中，我们提出了一种新的检索增强方法用于个性化回复生成。具体而言，我们设计了一个在对话域数据上训练的层次化Transformer检索器以执行个性化检索，并构建了一个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。在真实世界数据集上的大量实验证明了我们的模型在生成更流畅且个性化回复方面的有效性。我们通过一套人工与自动评估指标定量评估了模型性能，发现其在英文Reddit对话数据集上优于现有最先进的基线模型。