A chatbot using LSTM-based multi-layer embedding for elderly care

Abstract

According to demographic changes, the services designed for the elderly are becoming more needed than before and increasingly important. In previous work, social media or community-based question-answer data were generally used to build the chatbot. In this study, we collected the MHMC chitchat dataset from daily conversations with the elderly. Since people are free to say anything to the system, the collected sentences are converted into patterns in the preprocessing part to cover the variability of conversational sentences. Then, an LSTM-based multi-layer embedding model is used to extract the semantic information between words and sentences in a single turn with multiple sentences when chatting with the elderly. Finally, the Euclidean distance is employed to select a proper question pattern, which is further used to select the corresponding answer to respond to the elderly. For performance evaluation, five-fold cross-validation scheme was employed for training and evaluation. Experimental results show that the proposed method achieved an accuracy of 79.96% for top-1 response selection, which outperformed the traditional Okapi model.