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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
EditorsLei Wang, Minghui Dong, Yanfeng Lu, Haizhou Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages70-74
Number of pages5
Volume2018-January
ISBN (Electronic)9781538632758
DOIs
Publication statusPublished - 2018 Apr 10
Event5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore
Duration: 2017 Dec 82017 Dec 10

Other

Other5th International Conference on Orange Technologies, ICOT 2017
CountrySingapore
CitySingapore
Period17-12-0817-12-10

Fingerprint

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)

Cite this

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. In L. Wang, M. Dong, Y. Lu, & H. Li (Eds.), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 (Vol. 2018-January, pp. 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. editor / Lei Wang ; Minghui Dong ; Yanfeng Lu ; Haizhou Li. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 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, C-H, Huang, KY, Hong, QB & Wang, HM 2018, A chatbot using LSTM-based multi-layer embedding for elderly care. in L Wang, M Dong, Y Lu & H Li (eds), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 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. ed. / Lei Wang; Minghui Dong; Yanfeng Lu; Haizhou Li. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 70-74.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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