A Context-Aware Chatbot Using Multi-Layer Embedding and NTN-based Pattern Matching for Elderly Care

Abstract

According to demographic changes the services designed for elderly are more needed and important Previous studies show that there is a high relationship between loneliness gloom and disability for elderly In order to increase the affective interaction and reduce the loneliness and isolation of elderly this thesis develops a context-aware chatbot system for elderly care In previous work social media or community-based question-answer data were generally used to build the chatbots This kind of data is different from our daily conversation and lack of context information For elderly care the chatbot system should respond to the users with the sentences considering the causal context Therefore we collected the MHMC chatting dataset from daily communication Since people are free to say anything to the system the collected sentences are converted into patterns in the pre-processing part to cover the variability of spoken language Then an LSTM-based multi-layer embedding model is used to embed the semantic information between words and sentences in a single turn for context tracking Finally the Neural Tensor Network (NTN) is employed to select a proper response pattern which will be further filled with suitable words based on a rule-based method as the response to the elderly For performance evaluation this study collected an MHMC chatting dataset consisting of 65 topic-based dialogues and 2239 message-response pairs from 40 subjects Five-fold cross-validation scheme was employed for training and evaluation Experimental results show that the proposed method achieved an accuracy of 69 8% which outperformed the traditional Okapi model

Date of Award	2018 Apr 23
Original language	English
Supervisor	Chung-Hsien Wu (Supervisor)