A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairment (2023)

Abstract: 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 therapy 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 therapy 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 therapy principle and emotional support strategy of the target response. Then a decoder interacts with the perceived therapy 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.

## introduction dialogue systems have enjoyed rapid progress in recent years, through communication with humans to satisfy diverse needs @xcite . cognition stimulation of elders is a critical psychological therapy where dialogue systems serve as effective tools for restoring the cognition of older adults @xcite @xcite . some studies have shown that chit-chat can help older people with cognitive restoration (van rijn et @xcite . meanwhile, several studies have shown that emotional support is beneficial for maintaining or even increasing cognitive function in elders @xcite @xcite . nonetheless, there remains an open question on how to introduce emotional support and therapy principles simultaneously into chit-chat dialogue systems to provide cognitive recovery training for elders with cognitive impairment. one main obstacle to building cognitive dialogue is the lack of training corpora, especially in the chinese language. therefore, we first construct a chinese cs conversation (csconv) dataset, containing about 2.6k groups of dialogue data where each utterance is annotated with three types of labels, i.e., therapy principle, emotional labels, and emotional support strategy labels. to generate openended responses with emotional support strategies, we propose a multi-source knowledge fusion in a chinese cs dialogue (csd) system. we use jiagu foot_1 , a chinese nlp toolkit, to extract emotional words and keywords to form knowledge source and progressively mask the extracted knowledge on the encoder side, to increase the generalizability of the model. meanwhile, we adopt chinese emobank @xcite to calculate the weight value of each word in the utterance, so that the model pays more attention to words with high values. by introducing multiple sources of external knowledge, we greatly enrich the content of the conversation. moreover, we judge the content and emotions that elders express which is critical to generate satisfied responses, matching them with the cognitive therapeutic principles, and coming up with corresponding supporting strategies. at last, we design a multi-source interactive mechanism so that emotional support strategies and cognitive stimulation therapies can be reasonably combined to generate responses benefiting to mental health. figure 1 shows an example of a conversation with an elder based on the therapy principle. in summary, our contributions are as follows: (1) we construct a chinese cs-based conversation dataset to facilitate the following research; (2) we propose a progressive mask method for encoder modules, which enhances the generalizability on emotional knowledge and the applicability of the therapeutic conversations with elders; (3) we design a multi-source interactive method to model the interaction among encoder modules, decoder modules and external knowledge; (4) we conduct extensive experiments to demonstrate the effectiveness of the proposed csd. ## conclusion and outlook in this paper, we construct a chinese cs conversation dataset and propose a multi-source knowledge fusion method for cs dialogue. experimental results show that csd outperforms state-of-the-art models in terms of both automatic and human evaluations. extensive experiments verify the effectiveness of the progressive mask method and the three interaction ways of multi-source interactive decoder in csd. as for future work, we plan to construct larger datasets of mandarin and cantonese cs conversations to train models, and address the issue of therapy principle, emotional support recognition in reference context in dialogue.