RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation
This work addresses the problem of generating engaging and personalized dialogue responses for chatbots, representing an incremental improvement over existing methods.
The paper tackled the challenge of endowing chatbots with a consistent persona by proposing a retrieval-enhanced approach for personalized response generation, demonstrating effectiveness through experiments on a real-world dataset with superior performance on English Reddit conversations.
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.