RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized
  Dialogue Response Generation
Shuai Liu Hyundong J. Cho Marjorie Freedman Xuezhe Ma Jonathan May
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.