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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83630完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成 | zh_TW |
| dc.contributor.advisor | Li-Chen Fu | en |
| dc.contributor.author | 羅鈞凱 | zh_TW |
| dc.contributor.author | Jun-Kai Luo | en |
| dc.date.accessioned | 2023-03-19T21:12:25Z | - |
| dc.date.available | 2024-04-03 | - |
| dc.date.copyright | 2022-08-24 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | [1] Reblin, Maija, and Bert N. Uchino, "Social and emotional support and its implication for health," Current opinion in psychiatry, 21.2, pp. 201 (2008).
[2] Burleson, R. Brant, “Emotional support skills,” Handbook of Communication and Social Interaction Skills, pp. 569-612, Routledge (2003). [3] Spiro, Howard, “What is empathy and can it be taught?,” Annals of internal medicine, 116.10: pp. 843-846 (1992). [4] Suchman, A. L., K. Markakis, H. B. Beckman, and R. Frankel, “A model of empathic communication in the medical interview,”. Jama, 277.8, pp. 678-682 (1997). [5] Goldsmith, J. Daena, and Fitch Kristine, "The normative context of advice as social support.," Human communication research, 23.4: pp. 454-476 (1997). [6] Dahiya, Menal, "A tool of conversation: Chatbot," International Journal of Computer Sciences and Engineering, 5.5: pp. 158-161(2017). [7] Cameron G., D. Cameron, G. Megaw, R. Bond, M. Mulvenna, S. O’Neill, C. Armour, and M. McTear, “Assessing the usability of a chatbot for mental health care,” in International Conference on Internet Science, pp. 121-132. Springer, Cham (2018). [8] Bolger N., A. DeLongis, R. C. Kessler, and E. A. Schilling, “Effects of daily stress on negative mood,” Journal of personality and social psychology, 57.5: pp. 808 (1989). [9] De Graaf, Ma Maartje, Somaya Ben Allouch, and Tineke Klamer. "Sharing a life with Harvey: Exploring the acceptance of and relationship-building with a social robot.," Computers in human behavior, 43: pp. 1-14(2015). [10] Duffy B. R., C. Rooney, G. M. O'Hare, and R. O'Donoghue, “What is a social robot?,” in 10th Irish Conference on Artificial Intelligence & Cognitive Science, pp. 1-3 (1999). [11] Baecker A. N., D. Y. Geiskkovitch, A. L. González, and J. E. Young, “Emotional support domestic robots for healthy older adults: Conversational prototypes to help with loneliness.,” in Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 122-124 (2020). [12] Dino F., R. Zandie, H. Abdollahi, S. Schoeder, and M. H. Mahoor, “Delivering cognitive behavioral therapy using a conversational social robot..” in IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2089-2095 (2019). [13] Vaswani A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need.,” Advances in neural information processing systems, vol.30 (2017). [14] Devlin J., M. W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding.,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp, 4171-4186 (2018). [15] Radford A., J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners.” OpenAI blog, 1.8, pp. 9 (2019). [16] Shaw B. A., N. Krause, L. M. Chatters, C. M. Connell, and B. Ingersoll-Dayton, “Emotional support from parents early in life, aging, and health.,” Psychology and aging, 19:1, pp. 4 (2004). [17] Slevin M. L., S. E. Nichols, S. M. Downer, P. Wilson, T. A. Lister, S. Arnott, J. Maher, RL Souhami, J.S. Tobias, A.H. Goldstone, and M. Cody, “Emotional support for cancer patients: what do patients really want?,” British journal of cancer, 74:8, pp. 1275-1279 (1996). [18] T. W. Strine, D. P. Chapman, L. Balluz, and A. H. Mokdad, “Health-related quality of life and health behaviors by social and emotional support.,” Social psychiatry and psychiatric epidemiology, 43:2, pp. 151-159 (2008). [19] Cramer, Duncan, “Emotional support, conflict, depression, and relationship satisfaction in a romantic partner.,” The Journal of Psychology, 138.6: pp. 532-542 (2004). [20] Baecker, A. N., D. Y. Geiskkovitch, A. L. González, and J. E. Young, “Emotional support domestic robots for healthy older adults: Conversational prototypes to help with loneliness.,” in Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 122-124 (2020). [21] Burleson, R. Brant, “What counts as effective emotional support? Explorations of individual and situational differences.,” Studies in applied interpersonal communication, pp. 207-227 (2008). [22] Dino F., R. Zandie, H. Abdollahi, S. Schoeder, and M. H. Mahoor, “Delivering cognitive behavioral therapy using a conversational social robot.,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2089-2095 (2019). [23] Oh K. J., D. Lee, B. Ko, and H. J. Choi, “A chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation.” in 18th IEEE international conference on mobile data management, pp. 371-375 (2017). [24] Jeong, S., D. E. Logan, M. S. Goodwin, S. Graca, B. O'Connell, H. Goodenough, L. Anderson, N. Stenquist, K. Fitzpatrick, M. Zisook, L. Plummer, C. Breazeal, and P. Weinstock, “A social robot to mitigate stress, anxiety, and pain in hospital pediatric care.,” in Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts, pp. 103-104 (2015). [25] Hedayati, H., S. Bhaduri, T. Sumner, D. Szafir, and M. D. Gross, “HugBot: a soft robot designed to give human-like hugs.,” in Proceedings of the 18th ACM International Conference on Interaction Design and Children, pp. 556-561 (2019). [26] Rajabi, Zahra, Amarda Shehu, and Ozlem Uzuner. “A multi-channel bilstm-cnn model for multilabel emotion classification of informal text.,” IEEE 14th International Conference on Semantic Computing, pp. 303-306 (2020). [27] Luo, Linkai, and Yue Wang. “Emotionx-hsu: Adopting pre-trained bert for emotion classification.,” arXiv preprint arXiv:1907.09669 (2019). [28] Badaro G., H. Jundi, H. Hajj, and W. El-Hajj, “EmoWordNet: Automatic expansion of emotion lexicon using English WordNet.,” in Proceedings of the seventh joint conference on lexical and computational semantics, pp. 86-93 (2018). [29] Roller S., E. Dinan, N. Goyal, D. Ju, M. Williamson, Y. Liu, J. Xu, M. Ott, K. Shuster, S.M. Smith, Boureau Y-L., and J. Weston, “Recipes for building an open-domain chatbot.,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pp. 300-325 (2020). [30] Morris R. R., K. Kouddous, R. Kshirsagar, and S. M. Schueller, “Towards an artificially empathic conversational agent for mental health applications: system design and user perceptions.,” Journal of medical Internet research, 20:6, pp. e10148 (2018). [31] Mikolov T., K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space.,” arXiv preprint arXiv:1301.3781 (2013). [32] Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. "Glove: Global vectors for word representation." Proceedings of the 2014 conference on empirical methods in natural language processing, pp. 1532-1543 (2014). [33] Bojanowski P., E. Grave, A. Joulin, and T. Mikolov. “Enriching word vectors with subword information.” Transactions of the association for computational linguistics, vol. 5, pp. 135-146 (2017). [34] Liu Y., M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach.,” in Proceedings of the 20th Chinese National Conference on Computational Linguistics, pp. 1218-1217 (2019). [35] Cui Y., W. Che, T. Liu, B. Qin, and Z. Yang, “Pre-training with whole word masking for chinese bert.,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 3504-3514 (2021). [36] China Computer Federation, “Emotion Analysis in Chinese Weibo Text task.” CCF Conference on Natural Language Processing & Chinese Computing (2013), Retrieved April 14, 2022, from http://tcci.ccf.org.cn/conference/2013/pages/page04_eva.html [37] Yang, J. X, “GPT2 for Chinese chitchat.” Github, (2021), Retrieved February 10, 2022, from https://github.com/yangjianxin1/GPT2-chitchat [38] Rashkin, H., E. M. Smith, M. Li, and Y. L. Boureau, “Towards empathetic open-domain conversation models: A new benchmark and dataset.,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5370-5381 (2018). [39] Majumder N., P. Hong, S. Peng, J. Lu, D. Ghosal, A. Gelbukh, R. Mihalcea, and S. Poria, “MIME: MIMicking emotions for empathetic response generation.,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 8968-8979 (2020). [40] Li, Q., P. Li, Z. Ren, P. Ren, and Z. Chen. “Knowledge bridging for empathetic dialogue generation.,” arXiv preprint arXiv:2009.09708 (2022). [41] Gao, J., Y. Liu, H. Deng, W. Wang, Y. Cao, J. Du, and R. Xu, “Improving empathetic response generation by recognizing emotion cause in conversations.,” in Findings of the Association for Computational Linguistics: EMNLP ,pp. 807-819 (2021). [42] Speer, Robyn, Joshua Chin, and Catherine Havasi. “Conceptnet 5.5: An open multilingual graph of general knowledge.,” Thirty-first AAAI conference on artificial intelligence (2017). [43] Li, X., W. Gao, S. Feng, Y. Zhang, and D. Wang, “Boundary detection with bert for span-level emotion cause analysis.,” Association for Computational Linguistics (2021). [44] Gui, L., R. Xu, D. Wu, Q. Lu, and Y. Zhou, “Event-driven emotion cause extraction with corpus construction.,” in Social Media Content Analysis: Natural Language Processing and Beyond, pp. 145-160 (2018). [45] Ghazi, Diman, Diana Inkpen, and Stan Szpakowicz, “Detecting emotion stimuli in emotion-bearing sentences.,” International Conference on Intelligent Text Processing and Computational Linguistics, pp. 152-165, Springer, Cham, 2015. [46] Pudlinski, Christopher, “Doing empathy and sympathy: Caring responses to troubles tellings on a peer support line.,” Discourse studies, 7.3, pp. 267-288 (2005). [47] Singer, Tania, and Claus Lamm, “The social neuroscience of empathy.” Annals of the New York Academy of Sciences, 1156.1, pp. 81-96 (2009). [48] Reimers, Nils, and Iryna Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks.,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 3982-3992 (2019). [49] Zhang Y., S. Sun, M. Galley, Y. C. Chen, C. Brockett, X. Gao, J. Gao, J. Liu, and B. Dolan, “Dialogpt: Large-scale generative pre-training for conversational response generation.,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 270-278 (2019). [50] Liu C. W., Lowe R., Serban I. V., Noseworthy M., Charlin L., and Pineau J. “How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation.,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2122-2132 (2016). [51] Papineni K., Roukos S., Ward T., and Zhu W. J., “Bleu: a method for automatic evaluation of machine translation.,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 311-318 (2002). [52] Li J., Galley M., Brockett C., Gao J., and Dolan B., “A diversity-promoting objective function for neural conversation models.,” in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 110-119 (2015). [53] Demszky D., Movshovitz-Attias D., Ko J., Cowen A., Nemade G., and Ravi, S., “GoEmotions: A dataset of fine-grained emotions.,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4040-4054 (2020). [54] Loshchilov I., and Hutter F., “Decoupled weight decay regularization.,” in 7th International Conference on Learning Representations (2017). [55] Zhang T., Kishore V., Wu F., Weinberger K. Q., and Artzi Y., “Bertscore: Evaluating text generation with bert,” in 8th International Conference on Learning Representations (2019). | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83630 | - |
| dc.description.abstract | 情感支持是一項極為重要的技能，它能安慰正處於低潮的人。在現代高壓的社會之中，人們經常被各種各樣的壓力逼迫得喘不過氣。通常，我們能夠從朋友或家人的身上尋求情感支持，藉此度過自己艱難的時期。但實際上，不可能每次我們需要時，都有人在身邊提供幫助。在本篇論文中，我們提出了一個能夠根據使用者所說的話來產生情感支持回覆的系統。此系統使用深度學習的技術，以從使用者的話語中提取情緒和語意資訊。在接收到使用者的話語之後，系統會先辨識語句的情緒。根據不同的情緒，系統會調整使用不同的方法來產生適當的回覆給使用者。
同理心是指能夠理解別人的經歷、分享別人感受的一種能力。若要提供有效的情感支持，同理心是不可或缺的。若要表現出同理心，我們必須了解目標對象的情緒以及其產生該情緒的原因。除此之外，若能對其經歷的情況具備一定的知識，也能夠使我們更加了解對象所經歷的事情、進而提供更好的情感支持。在我們提出的系統中，設計了一個能夠根據使用者輸入，整合使用者情緒、情緒產生原因、額外知識等資訊，並產生具有同理心回覆的深度學習模型。 | zh_TW |
| dc.description.abstract | Emotional support is an important skill that comforts people when depressed. In the stressful society, people are often overwhelmed by various pressure. Usually, emotional support from our friends or family can help us get through the tough time. However, it is unlikely to have people constantly provide emotional support when we are in need. In this thesis, we propose a system that can automatically generate emotional support responses according to the utterance from the user. The system uses deep learning techniques to extract both emotional and semantic information of the user’s utterances. After receiving the utterance from the user, the system will first recognize the emotion of it. According to different emotion, the system will adapt its strategy to generate an appropriate response to the user.
Empathy is the ability to understand other people’s experience and share their feelings, it is necessary if we want to provide effective emotional support. To show empathy, we need to understand the recipient’s emotion meanwhile the cause of the emotion. Moreover, having the background knowledge about the recipient helps us understand the recipient’s experience and provide better emotional support. In our proposed system, we design a deep learning model that integrates the information of user’s emotion, its cause and external knowledge to generate empathetic responses to user’s utterance. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T21:12:25Z (GMT). No. of bitstreams: 1 U0001-1908202210090100.pdf: 2371424 bytes, checksum: e15f933c3970f27620ec05de76308d6a (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES vii LIST OF TABLES viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Related work 4 1.3.1 Emotional Support 4 1.3.2 Emotion Classification 7 1.3.3 Dialogue System 9 1.4 Objectives and Contributions 11 1.5 Thesis organization 12 Chapter 2 Preliminaries 14 2.1 Emotional Support 14 2.2 Neural Networks 16 2.2.1 Multi-Layer Perceptron 17 2.2.2 Self-Attention 18 2.2.3 Transformer 20 2.3 Natural Language Processing 23 2.3.1 Natural Language Understanding 23 2.3.2 Natural Language Generation 28 Chapter 3 Emotion Recognition and Empathetic Response Generation 31 3.1 System Overview 31 3.2 Emotion Recognition 33 3.3 Emotion Cause Extraction 38 3.4 External Knowledge Extraction 43 3.5 Empathetic Response Generation 44 3.6 Chit-Chat Module 50 Chapter 4 Experiments 52 4.1 Experimental Setup 52 4.1.1 Implementation Details 52 4.1.2 Datasets 53 4.1.3 Evaluation Metrics 55 4.1.4 Comparison Methods 59 4.2 Results and discussion 60 4.2.1 Automatic Evaluation Results 60 4.2.2 Human Evaluation Results 62 4.3 Ablation Study 65 4.4 Human System Interaction 66 Chapter 5 Conclusions 72 REFERENCE 74 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 情感支持 | zh_TW |
| dc.subject | 情緒分類 | zh_TW |
| dc.subject | 同理心 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 情緒原因提取 | zh_TW |
| dc.subject | Emotional Support | en |
| dc.subject | Deep Learning | en |
| dc.subject | Natural Language Processing | en |
| dc.subject | Empathy | en |
| dc.subject | Emotion Classification | en |
| dc.subject | Emotion cause extraction | en |
| dc.title | 使用深度學習技術之具有同理心的情感支持系統 | zh_TW |
| dc.title | Emotional Support System with Empathy using Deep Learning Techniques | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 110-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蘇木春;陳縕儂;葉素玲;吳恩賜 | zh_TW |
| dc.contributor.oralexamcommittee | Mu-Chun Su;Yun-Nung Chen;Su-Ling Yeh;Joshua O. Goh | en |
| dc.subject.keyword | 情感支持,深度學習,自然語言處理,同理心,情緒分類,情緒原因提取, | zh_TW |
| dc.subject.keyword | Emotional Support,Deep Learning,Natural Language Processing,Empathy,Emotion Classification,Emotion cause extraction, | en |
| dc.relation.page | 81 | - |
| dc.identifier.doi | 10.6342/NTU202202571 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2022-08-22 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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