Recently, the topic-grounded dialogue (TGD) system has become increasingly
popular as its powerful capability to actively guide users to accomplish
specific tasks through topic-guided conversations. Most existing works utilize
side information (\eg topics or personas) in isolation to enhance the topic
selection ability. However, due to disregarding the noise within these
auxiliary information sources and their mutual influence, current models tend
to predict user-uninteresting and contextually irrelevant topics. To build
user-engaging and coherent dialogue agent, we propose a \textbf{P}ersonalized
topic s\textbf{E}lection model for \textbf{T}opic-grounded \textbf{D}ialogue,
named \textbf{PETD}, which takes account of the interaction of side information
to selectively aggregate such information for more accurately predicting
subsequent topics. Specifically, we evaluate the correlation between global
topics and personas and selectively incorporate the global topics aligned with
user personas. Furthermore, we propose a contrastive learning based persona
selector to filter out irrelevant personas under the constraint of lacking
pertinent persona annotations. Throughout the selection and generation, diverse
relevant side information is considered. Extensive experiments demonstrate that
our proposed method can generate engaging and diverse responses, outperforming
state-of-the-art baselines across various evaluation metrics.

提出了一种个性化话题选择模型 PETD，通过考虑边缘信息的相互作用，选择性地聚合这些信息以更准确地预测后续的话题，从而构建了一个富有吸引力和连贯性的对话系统。实验证明了该方法在各种评价指标上优于现有基线模型。

面向话题引导对话的个性化话题选择模型

Personalized Topic Selection Model for Topic-Grounded Dialogue

Recently, topic-grounded dialogue system has attracted significant attention
due to its effectiveness in predicting the next topic to yield better responses
via the historical context and given topic sequence. However, almost all
existing topic prediction solutions focus on only the current conversation and
corresponding topic sequence to predict the next conversation topic, without
exploiting other topic-guided conversations which may contain relevant
topic-transitions to current conversation. To address the problem, in this
paper we propose a novel approach, named Sequential Global Topic Attention
(SGTA) to exploit topic transition over all conversations in a subtle way for
better modeling post-to-response topic-transition and guiding the response
generation to the current conversation. Specifically, we introduce a latent
space modeled as a Multivariate Skew-Normal distribution with hybrid kernel
functions to flexibly integrate the global-level information with
sequence-level information, and predict the topic based on the distribution
sampling results. We also leverage a topic-aware prior-posterior approach for
secondary selection of predicted topics, which is utilized to optimize the
response generation task. Extensive experiments demonstrate that our model
outperforms competitive baselines on prediction and generation tasks.

本文提出一种名为 Sequential Global Topic Attention（SGTA）的新方法，将所有对话中主题转换的全局信息柔和地利用起来，以更好地建模以及引导当前对话的回复生成，采用与混合内核函数建模的多变量偏态正态分布的潜在空间预测主题，并利用主题感知的先验 - 后验方法进行次要预测主题的选择，实验证明 SGTA 方法在预测和生成任务上优于竞争基准。