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

Ming Hsiang Su, Chung Hsien Wu, Kun Yi Huang, Qian Bei Hong, Hsin Min Wang

研究成果: Conference contribution

3 引文 (Scopus)

摘要

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.

原文English
主出版物標題Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
編輯Lei Wang, Minghui Dong, Yanfeng Lu, Haizhou Li
發行者Institute of Electrical and Electronics Engineers Inc.
頁面70-74
頁數5
2018-January
ISBN(電子)9781538632758
DOIs
出版狀態Published - 2018 四月 10
事件5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore
持續時間: 2017 十二月 82017 十二月 10

Other

Other5th International Conference on Orange Technologies, ICOT 2017
國家Singapore
城市Singapore
期間17-12-0817-12-10

指紋

sentences
embedding
Semantics
Social Media
conversation
evaluation
semantics
preprocessing
social media
population development
education
Demography
community
performance

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Instrumentation
  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems
  • Health(social science)

引用此文

Su, M. H., Wu, C. H., Huang, K. Y., Hong, Q. B., & Wang, H. M. (2018). A chatbot using LSTM-based multi-layer embedding for elderly care. 於 L. Wang, M. Dong, Y. Lu, & H. Li (編輯), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 (卷 2018-January, 頁 70-74). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICOT.2017.8336091
Su, Ming Hsiang ; Wu, Chung Hsien ; Huang, Kun Yi ; Hong, Qian Bei ; Wang, Hsin Min. / A chatbot using LSTM-based multi-layer embedding for elderly care. Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. 編輯 / Lei Wang ; Minghui Dong ; Yanfeng Lu ; Haizhou Li. 卷 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. 頁 70-74
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title = "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.",
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Su, MH, Wu, CH, Huang, KY, Hong, QB & Wang, HM 2018, A chatbot using LSTM-based multi-layer embedding for elderly care. 於 L Wang, M Dong, Y Lu & H Li (編輯), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. 卷 2018-January, Institute of Electrical and Electronics Engineers Inc., 頁 70-74, 5th International Conference on Orange Technologies, ICOT 2017, Singapore, Singapore, 17-12-08. https://doi.org/10.1109/ICOT.2017.8336091

A chatbot using LSTM-based multi-layer embedding for elderly care. / Su, Ming Hsiang; Wu, Chung Hsien; Huang, Kun Yi; Hong, Qian Bei; Wang, Hsin Min.

Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. 編輯 / Lei Wang; Minghui Dong; Yanfeng Lu; Haizhou Li. 卷 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 70-74.

研究成果: Conference contribution

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N2 - 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.

AB - 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.

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Su MH, Wu CH, Huang KY, Hong QB, Wang HM. A chatbot using LSTM-based multi-layer embedding for elderly care. 於 Wang L, Dong M, Lu Y, Li H, 編輯, Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. 卷 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 70-74 https://doi.org/10.1109/ICOT.2017.8336091