Personalized Dialogue Generation (PDG) aims to create coherent responses
according to roles or personas. Traditional PDG relies on external role data,
which can be scarce and raise privacy concerns. Approaches address these issues
by extracting role information from dialogue history, which often fail to
generically model roles in continuous space. To overcome these limitations, we
introduce a novel framework \textbf{MO}dels \textbf{R}oles from
\textbf{P}ersonalized Dialogue \textbf{H}istory by \textbf{E}xploring and
\textbf{U}tilizing Latent \textbf{S}pace (MORPHEUS) through a three-stage
training process. Specifically, we create a persona codebook to represent roles
in latent space compactly, and this codebook is used to construct a posterior
distribution of role information. This method enables the model to generalize
across roles, allowing the generation of personalized dialogues even for unseen
roles. Experiments on both Chinese and English datasets demonstrate that
MORPHEUS enhances the extraction of role information, and improves response
generation without external role data. Additionally, MORPHEUS can be considered
an efficient fine-tuning for large language models.

通过在对话历史中提取角色信息并构建潜在空间中的角色代码书，MORPHEUS 框架通过三阶段训练过程使模型能够泛化到不同角色，从而实现个性化对话生成，无需外部角色数据，并且可以作为大型语言模型的高效微调。

MORPHEUS：从个性化对话历史中探索和利用潜在空间建模角色

MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring  and Utilizing Latent Space

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.
this https URL

研究了通过利用人物背景资料和对话背景生成高度个性化的回应的个性化对话生成。提出了一个称为 $	extbf {LAPDOG}$ 的模型，该模型利用外部知识来生成个性化对话。LAPDOG 模型由一个故事检索器和一个对话生成器组成，其中故事检索器利用给定的人物背景资料从故事文档中检索相关信息，对个性化对话进行补充。对话生成器利用对话历史和增强的人物背景资料来生成个性化的回应。实验结果表明，LAPDOG 方法在 CONVAI2 数据集上显著优于基线方法，证明了该方法的有效性。

个性化对话生成的检索增强学习

Learning Retrieval Augmentation for Personalized Dialogue Generation

Maintaining a consistent persona is essential for building a human-like
conversational model. However, the lack of attention to the partner makes the
model more egocentric: they tend to show their persona by all means such as
twisting the topic stiffly, pulling the conversation to their own interests
regardless, and rambling their persona with little curiosity to the partner. In
this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue
generation Across both partY personas) that considers both parties as a "team":
expressing self-persona while keeping curiosity toward the partner, leading
responses around mutual personas, and finding the common ground. Specifically,
we first represent self-persona, partner persona and mutual dialogue all in the
concept sets. Then, we propose the Concept Set framework with a suite of
knowledge-enhanced operations to process them such as set algebras, set
expansion, and set distance. Based on these operations as medium, we train the
model by utilizing 1) concepts of both party personas, 2) concept relationship
between them, and 3) their relationship to the future dialogue. Extensive
experiments on a large public dataset, Persona-Chat, demonstrate that our model
outperforms state-of-the-art baselines for generating less egocentric, more
human-like, and higher quality responses in both automatic and human
evaluations.

本研究提出了一种基于概念集的个性化对话生成方法，试图实现构建一个更符合人类行为的对话模型；该方法关注两个交谈方的对话，通过表达个人形象、保持对交谈对象的好奇心、引导交谈回到共同主题等方式来减少自我中心的表现，并结合概念集框架进行处理、训练，取得了在公共数据集 Persona-Chat 上优秀的成果。