  This paper presents a method for building a personalized open-domain dialogue
system to address the WWH (WHAT, WHEN, and HOW) problem for natural response
generation in a commercial setting, where personalized dialogue responses are
heavily interleaved with casual response turns. The proposed approach involves
weighted dataset blending, negative persona information augmentation methods,
and the design of personalized conversation datasets to address the challenges
of WWH in personalized, open-domain dialogue systems. Our work effectively
balances dialogue fluency and tendency to ground, while also introducing a
response-type label to improve the controllability and explainability of the
grounded responses. The combination of these methods leads to more fluent
conversations, as evidenced by subjective human evaluations as well as
objective evaluations.
