Endowing a dialogue system with particular personality traits is essential to
deliver more human-like conversations. However, due to the challenge of
embodying personality via language expression and the lack of large-scale
persona-labeled dialogue data, this research problem is still far from
well-studied. In this paper, we investigate the problem of incorporating
explicit personality traits in dialogue generation to deliver personalized
dialogues.
To this end, firstly, we construct PersonalDialog, a large-scale multi-turn
dialogue dataset containing various traits from a large number of speakers. The
dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers.
Each utterance is associated with a speaker who is marked with traits like Age,
Gender, Location, Interest Tags, etc. Several anonymization schemes are
designed to protect the privacy of each speaker. This large-scale dataset will
facilitate not only the study of personalized dialogue generation, but also
other researches on sociolinguistics or social science.
Secondly, to study how personality traits can be captured and addressed in
dialogue generation, we propose persona-aware dialogue generation models within
the sequence to sequence learning framework. Explicit personality traits
(structured by key-value pairs) are embedded using a trait fusion module.
During the decoding process, two techniques, namely persona-aware attention and
persona-aware bias, are devised to capture and address trait-related
information. Experiments demonstrate that our model is able to address proper
traits in different contexts. Case studies also show interesting results for
this challenging research problem.

本研究旨在研究将个性特征应用于对话生成中以提供个性化对话的问题，并提出了一种基于关键 - 值对的嵌入式特征融合模块和两种新颖的特征感知机制，即基于对话者的关注机制和基于对话者的偏置机制，并构建了 PersonalDialog 数据集，该数据集包含来自海量讲话者的具有不同特征的大量多轮对话。实验结果表明，所提出的模型能够在不同情境下处理适当的特征。