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

Leveraging persona information of users in Neural Response Generators (NRG)
to perform personalized conversations has been considered as an attractive and
important topic in the research of conversational agents over the past few
years. Despite of the promising progresses achieved by recent studies in this
field, persona information tends to be incorporated into neural networks in the
form of user embeddings, with the expectation that the persona can be involved
via the End-to-End learning. This paper proposes to adopt the
personality-related characteristics of human conversations into variational
response generators, by designing a specific conditional variational
autoencoder based deep model with two new regularization terms employed to the
loss function, so as to guide the optimization towards the direction of
generating both persona-aware and relevant responses. Besides, to reasonably
evaluate the performances of various persona modeling approaches, this paper
further presents three direct persona-oriented metrics from different
perspectives. The experimental results have shown that our proposed methodology
can notably improve the performance of persona-aware response generation, and
the metrics are reasonable to evaluate the results.

本文提出采用条件变异自编码器设计的深度学习模型，引入人格特征以及两个新的正则化项，以生成既考虑话语的人格又相关的回答，进而改善了基于用户画像的回答生成表现，并提出了三个用户画像定向指标，验证了该方法的有效性。

指导变分响应生成器利用人物特点

Guiding Variational Response Generator to Exploit Persona

Tips, as a compacted and concise form of reviews, were paid less attention by
researchers. In this paper, we investigate the task of tips generation by
considering the `persona' information which captures the intrinsic language
style of the users or the different characteristics of the product items. In
order to exploit the persona information, we propose a framework based on
adversarial variational auto-encoders (aVAE) for persona modeling from the
historical tips and reviews of users and items. The latent variables from aVAE
are regarded as persona embeddings. Besides representing persona using the
latent embeddings, we design a persona memory for storing the persona related
words for users and items. Pointer Network is used to retrieve persona wordings
from the memory when generating tips. Moreover, the persona embeddings are used
as latent factors by a rating prediction component to predict the sentiment of
a user over an item. Finally, the persona embeddings and the sentiment
information are incorporated into a recurrent neural networks based tips
generation component. Extensive experimental results are reported and discussed
to elaborate the peculiarities of our framework.

通过历史数据中的 `persona' 信息，提出了一个基于对抗自编码器的框架，用于将信息可视化为潜在变量，并开发了一个新的记忆储存方案，用于存储用户和项目的相关信息，然后使用递归神经网络生成出反映个性化评价的广告语。