The personalized dialogue explores the consistent relationship between
dialogue generation and personality. Existing personalized dialogue agents
model persona profiles from three resources: sparse or dense persona
descriptions and dialogue histories. However, sparse structured persona
attributes are explicit but uninformative, dense persona texts contain rich
persona descriptions with much noise, and dialogue history query is both noisy
and uninformative for persona modeling. In this work, we combine the advantages
of the three resources to obtain a richer and more accurate persona. We design
a Contrastive Latent Variable-based model (CLV) that clusters the dense persona
descriptions into sparse categories, which are combined with the history query
to generate personalized responses. Experimental results on Chinese and English
datasets demonstrate our model's superiority in personalization.

本研究通过使用对比潜变量模型来将浓缩的用户描述信息聚类到稀疏的分类中，并与对话历史查询相结合，以生成更具个性化的响应。实验结果表明，该模型在个性化方面的优势在中英文数据集上都得到了证实。

使用对比潜在变量增强个性化对话生成：结合稀疏和稠密人物角色

Enhancing Personalized Dialogue Generation with Contrastive Latent  Variables: Combining Sparse and Dense Persona

We have a Christmas gift for Harry Potter fans all over the world. In this
paper, we present Harry Potter Dialogue (HPD), a dataset that helps train Harry
Potter-like dialogue agents. Such a task is typically viewed as a variant of
personalized dialogue agents, but they differ significantly in three respects:
1) Harry lived in a virtual world of wizards, thus, real-world commonsense may
not apply to Harry's conversations; 2) Harry's behavior is strongly linked to
background information in conversations: the scene, its attributes and its
relationship to other speakers; and 3) Such backgrounds are dynamically altered
as the storyline goes on. The HPD dataset, as the first dataset to facilitate
the study of dialogue agent construction for characters within a story,
provides rich contextual information about each dialogue session such as
scenes, character attributes, and relations. More importantly, all the
background information will change over the course of the story. In addition,
HPD could support both dialogue generation and retrieval tasks. We evaluate
baselines such as Dialog-GPT and BOB to determine the extent to which they can
generate Harry Potter-like responses. The experimental results disappoint us in
that although the generated responses are fluent, they still seem out of
character for Harry. Besides, we validate the current most robust dialogue
agent, ChatGPT, which also can't generate plausible Harry-Potter-like responses
in some cases, either. Our results suggest that there is much scope for future
research.

本论文介绍了 Harry Potter Dialogue (HPD) 数据集，用于训练类似 Harry Potter 的对话代理。与个性化对话代理不同，Harry Potter 是一个虚拟魔法世界中的角色，其对话具有丰富而动态变化的背景信息。实验结果表明现有的模型难以生成类似于 Harry Potter 的回复，为未来的研究提供了广阔的空间。

构建故事角色对话系统：以哈里・波特为例

What would Harry say? Building Dialogue Agents for Characters in a Story

Current works in the generation of personalized dialogue primarily contribute
to the agent presenting a consistent personality and driving a more informative
response. However, we found that the generated responses from most previous
models tend to be self-centered, with little care for the user in the dialogue.
Moreover, we consider that human-like conversation is essentially built based
on inferring information about the persona of the other party. Motivated by
this, we propose a novel personalized dialogue generator by detecting an
implicit user persona. Because it is hard to collect a large number of detailed
personas for each user, we attempted to model the user's potential persona and
its representation from dialogue history, with no external knowledge. The
perception and fader variables were conceived using conditional variational
inference. The two latent variables simulate the process of people being aware
of each other's persona and producing a corresponding expression in
conversation. Finally, posterior-discriminated regularization was presented to
enhance the training procedure. Empirical studies demonstrate that, compared to
state-of-the-art methods, our approach is more concerned with the user's
persona and achieves a considerable boost across the evaluations.

提出了一种基于用户人格检测的个性化对话生成器，该方法使用条件变分推理建立用户潜在人格模型并利用后验鉴别正则化提高训练效果，在实证研究中实现比现有技术更高的对用户人格关注和提升了对话品质的效果。