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 language | English |
|---|---|
| Title of host publication | Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 |
| Editors | Lei Wang, Minghui Dong, Yanfeng Lu, Haizhou Li |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 70-74 |
| Number of pages | 5 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538632758 |
| DOIs | |
| Publication status | Published - 2018 Apr 10 |
| Event | 5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore Duration: 2017 Dec 8 → 2017 Dec 10 |
Other
| Other | 5th International Conference on Orange Technologies, ICOT 2017 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 17-12-08 → 17-12-10 |
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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
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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 proceeding › Conference contribution
TY - GEN
T1 - A chatbot using LSTM-based multi-layer embedding for elderly care
AU - Su, Ming Hsiang
AU - Wu, Chung-Hsien
AU - Huang, Kun Yi
AU - Hong, Qian Bei
AU - Wang, Hsin Min
PY - 2018/4/10
Y1 - 2018/4/10
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.
UR - http://www.scopus.com/inward/record.url?scp=85048553973&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048553973&partnerID=8YFLogxK
U2 - 10.1109/ICOT.2017.8336091
DO - 10.1109/ICOT.2017.8336091
M3 - Conference contribution
VL - 2018-January
SP - 70
EP - 74
BT - Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
A2 - Wang, Lei
A2 - Dong, Minghui
A2 - Lu, Yanfeng
A2 - Li, Haizhou
PB - Institute of Electrical and Electronics Engineers Inc.
ER -