  Personalized dialogue generation, focusing on generating highly tailored
responses by leveraging persona profiles and dialogue context, has gained
significant attention in conversational AI applications. However, persona
profiles, a prevalent setting in current personalized dialogue datasets,
typically composed of merely four to five sentences, may not offer
comprehensive descriptions of the persona about the agent, posing a challenge
to generate truly personalized dialogues. To handle this problem, we propose
$\textbf{L}$earning Retrieval $\textbf{A}$ugmentation for
$\textbf{P}$ersonalized $\textbf{D}$ial$\textbf{O}$gue $\textbf{G}$eneration
($\textbf{LAPDOG}$), which studies the potential of leveraging external
knowledge for persona dialogue generation. Specifically, the proposed LAPDOG
model consists of a story retriever and a dialogue generator. The story
retriever uses a given persona profile as queries to retrieve relevant
information from the story document, which serves as a supplementary context to
augment the persona profile. The dialogue generator utilizes both the dialogue
history and the augmented persona profile to generate personalized responses.
For optimization, we adopt a joint training framework that collaboratively
learns the story retriever and dialogue generator, where the story retriever is
optimized towards desired ultimate metrics (e.g., BLEU) to retrieve content for
the dialogue generator to generate personalized responses. Experiments
conducted on the CONVAI2 dataset with ROCStory as a supplementary data source
show that the proposed LAPDOG method substantially outperforms the baselines,
indicating the effectiveness of the proposed method. The LAPDOG model code is
publicly available for further exploration.
https://github.com/hqsiswiliam/LAPDOG
