  When communicating with elders with cognitive impairment, cognitive
stimulation (CS) help to maintain the cognitive health of elders. Data sparsity
is the main challenge in building CS-based dialogue systems, particularly in
the Chinese language. To fill this gap, we construct a Chinese CS conversation
(CSConv) dataset, which contains about 2.6K groups of dialogues with CS
principles and emotional support strategy labels. Making chit chat while
providing emotional support is overlooked by the majority of existing cognitive
dialogue systems. In this paper, we propose a multi-source knowledge fusion
method for CS dialogue (CSD), to generate open-ended responses guided by the CS
principle and emotional support strategy. We first use a progressive mask
method based on external knowledge to learn encoders as effective classifiers,
which is the prerequisite to predict the CS principle and emotional support
strategy of the target response. Then a decoder interacts with the perceived CS
principle and emotional support strategy to generate responses. Extensive
experiments conducted on the CSConv dataset demonstrate the effectiveness of
the proposed method, while there is still a large space for improvement
compared to human performance.
