Abstract

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.

Date

June 12, 2023

Authors

Shuai Liu, Hyundong J Cho, Marjorie Freedman, Xuezhe Ma, Jonathan May

Conference

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)