  Understanding rich dialogues often requires NLP systems to access relevant
commonsense persona knowledge, but retrieving this knowledge is challenging due
to complex contexts and the implicit nature of commonsense. This paper presents
our approach to the Commonsense Persona Knowledge Linking (CPKL) challenge,
addressing the critical need for integrating persona and commonsense knowledge
in open-domain dialogue systems. We introduce SynCPKL Pipeline, a pipeline that
leverages Large Language Models to generate high-quality synthetic datasets for
training commonsense persona knowledge linkers. To demonstrate the efficacy of
our approach, we present SynCPKL, a new dataset specifically designed for this
task. Our experiments validate the effectiveness of SynCPKL for training
commonsense persona knowledge linkers. Additionally, our top-performing model,
Derberta-SynCPKL, secured first place in the CPKL challenge by a 16%
improvement in F1 score. We released both SynCPKL and Derberta-SynCPKL at
https://github.com/irislin1006/CPKL.
