  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.
