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.

翻译：在与认知障碍老人沟通时，认知刺激（CS）有助于维护其认知健康。数据稀疏性是构建基于认知刺激的对话系统面临的主要挑战，尤其是在中文语境下。为填补这一空白，我们构建了中文认知刺激对话（CSConv）数据集，包含约2600组带认知刺激原则与情感支持策略标签的对话组。现有大多数认知对话系统忽视了在闲谈过程中提供情感支持的问题。本文提出一种用于认知刺激对话（CSD）的多源知识融合方法，以在认知刺激原则和情感支持策略引导下生成开放式回复。我们首先基于外部知识采用渐进式掩码方法学习编码器作为高效分类器，这是预测目标回复的认知刺激原则和情感支持策略的前提条件。随后解码器与感知到的认知刺激原则及情感支持策略进行交互以生成回复。在CSConv数据集上开展的大量实验证明了所提方法的有效性，但与人类表现相比仍存在较大提升空间。