整合聊天機器人與語音對話機器人: 打造K12數位學習助理

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姓名

趙啟雲(CHI-YUN CHAO) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

整合聊天機器人與語音對話機器人: 打造K12數位學習助理
(E-Learning Assistant in K12 Academy: Integrating Chatbot and Voice Dialogue Robot)

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摘要(中)

近年來數位學習越來越流行，因為數位學習有著能隨時隨地地進行學習的優點，相較於過去傳統教室老師對學生的教育學習者也可以針對自己的學習狀況，學生可以更加隨性調整學習進度，可以有效地增加學習上的效率。一個好的數位學習平台，線上老師客服是必不可少的，但由於龐大的使用客群，使用真人老師全天候的服務所有的學生成本非常高，因此我們開發了人工智慧教學助理系統，可以隨時隨地在社交通訊軟體上分擔線上老師客服的工作:為學生解決課業上的問題、解決學生數位平台的操作問題。為了更方便學生使用，我們甚至可以透過語音對話機器人，直接用語音對話方式為學生提供服務，提供更完善的數位學習環境。
本文提出的聊天機器人是一個檢索式模型，先藉由隱馬可爾科夫模型(Hidden Markov Model)將使用者的輸入分詞後，除去常用詞，提取出關鍵字，再將關鍵字透過詞向量化模型(Word2Vec Model)比對各個資料庫中定義好的對話主題的相似度，以此實做對使用者對話的關鍵字提取以及分類功能。

摘要(英)

In recent years, E-Learning has become more and more popular because of being able to let students learn anytime and anywhere. Compared with traditional classroom learning, students can adjust their learning more casually according to their own learning conditions. It can increase the efficiency of their learning. For a good e-learning platform, on-line tutor service is need. But hiring human tutors will cost a lot due to large number of students in whole time use. Therefore, we develop a chatbot system to share some of the work of human tutors: solve problems in the course for students and provide FAQ of the operation in the e-learning platform. It can even provide service to students via voice dialogue robots. With our system, students can get a more complete e-learning environment.
The chatbot proposed in this paper is a retrieval-based model. After the user′s input is segmented by the Hidden Markov Model to get the keywords, and the keywords are transmitted through the Word2vec model. The Word2Vec Model compares the similarity of the conversation topics defined in each database to achieve keyword extraction and classification for user conversations

關鍵字(中)

★ 機器學習
★ 隱馬可爾科夫模型
★ 數位學習
★ 詞向量神經網路

關鍵字(英)

★ Machine learning
★ E-Learning
★ word2vec neural network
★ hidden Markovkov model

論文目次

摘要 I
Abstract II
致謝 III
Table of Contents IV
List of Figures VI
1. Introduction 1
1.1 Motivation 1
1.2 Organization of Thesis 6
2. Related Works 7
2.1 E-Learning Research 7
2.2 Chatbot 11
2.3 Speech recognition 13
3. System Design 14
3.1 Background 14
3.2 System Architecture 15
3.2.1 State identify 19
3.2.2 Voice Recognition System 23
4. Implementation 24
4.1 Implementation Overview 24
4.2 Chat Bot System module 24
4.3 Web Server module 26
4.4 Voice Dialogue Robot module 26
4.5 Extend Chatbot Response Module 27
4.6 Training Data set for Word2Vec model 28
4.7 Results and Displays 29
5. Conclusion and Future Works 33
References 34

參考文獻

[1] The Summit Learning Platform [online]. https://www.summitlearning.org/program/online-platform#.
[2] Blanka Klimova and Petra Maresova, “Cloud Computing and E-Learning and Their Benefits for the Institutions of Higher Learning”, IEEE Conference on e-Learning, e-Management and e-Services, 2016.
[3] Herminio Garcia-Gonzalez, Jose Emilio Labra Gayo and MPuerto Paule-Ruiz, “Enhancing e-Learning Content by Using Semantic Web Technologies”, IEEE Transactions on Learning Technologies ( Volume: 10, Issue: 4, Oct.-Dec. 1 2017 ).
[4] Pedro J. Munoz-Merino, Carlos Delgado Kloos and Mario Munoz-Organero, “Enhancement of Student Learning Through the Use of a Hinting Computer e-Learning System and Comparison With Human Teachers”, IEEE Transactions on Education ( Volume: 54, Issue: 1, Feb. 2011 )
[5] Docebo-eLearning-Trends-for-2018 whitepaper [online]. https://www.docebo.com/resource/whitepaper-elearning-trends-2018/
[6] Ron Austin, Mak Sharma, Philip Moore and David Newell, “Situated Computing and Virtual Learning Environments: e-Learning and the Benefits to the Students Learning”, Seventh International Conference on Complex, Intelligent, and Software Intensive Systems, 2013.
[7] Matthew Montebello, “E-learning paradigms: A model to address known issues”, Computing Conference, 2017.
[8] Hosam Al-Samarraie, Hassan Selim, Timothy Teo & Fahed Zaqout (2017), “Isolation and distinctiveness in the design of e-learning systems influence user preferences, Interactive Learning Environments”, 25:4, 452-466, DOI: 10.1080/10494820.2016.1138313
[9] Bousaaid Mourad, Ayaou Tarik, Afdel Karim and Estraillier Pascal, “System Interactive Cyber Presence for E- Learning to Break Down Learner Isolation”, International Journal of Computer Applications (0975 – 8887). Volume 111 – No 16, February 2015.
[10] AlphaGo, Deepmind [online]. https://deepmind.com/research/alphago/
[11] Benedict du Boulay, “Artificial Intelligence as an Effective Classroom Assistant”, IEEE Intelligent Systems ( Volume: 31, Issue: 6, Nov.-Dec. 2016 )
[12] Carlos Ramos, Juan Carlos Augusto, Daniel Shapiro, “Ambient Intelligence—the Next Step for Artificial Intelligence”, IEEE Intelligent Systems ( Volume: 23, Issue: 2, March-April 2008 )
[13] Google assistant [online]. https://assistant.google.com/
[14] Google I/O [online]. https://events.google.com/io/
[15] Heung-Yeung Shum, Xiaodong He, Di Li, “From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots”, arXiv:1801.01957 [cs.AI] 6 Jan 2018
[16] Apple, Siri [online]. https://www.apple.com/ios/siri/
[17] Amazon, Alexa [online]. https://developer.amazon.com/alexa
[18] Microsoft XiaoIce [online]. http://www.msxiaoice.com/
[19] Microsoft Cortana [online]. https://www.microsoft.com/en-us/cortana
[20] Forbes Aisa BOT [online]. https://www.messenger.com/t/forbesasia
[21] TaxiGo BOT[online]. https://www.messenger.com/t/taxigo.tw
[22] Kamigo BOT[online]. https://www.kamigo.tw/
[23] Facebook, messenger [online]. https://messenger.fb.com/
[24] Telegram [online]. https://telegram.org/blog/bot-revolution
[25] Line [online]. https://line.me/en/
[26] Got It Study [online]. http://expert.gotitapp.co/
[27] Deakin Genie [online]. https://www.youtube.com/watch?v=zsRPuU53E74
[28] Jill Watson [online]. https://pe.gatech.edu/blog/meet-jill-watson-georgia-techs-first-ai-teaching-assistant
[29] IBM Watson [online]. https://www.ibm.com/watson/
[30] Hao Fang, Hao Cheng, Maarten Sap, Elizabeth Clark, Ari Holtzman, Yejin Choi, Noah A. Smith, Mari Ostendorf, “Sounding Board: A User-Centric and Content-Driven Social Chatbot”, arXiv:1804.10202v1 [cs.HC] 26 Apr 2018
[31] Shih-syuan Huang, “A Flexible and Lightweight User-demand DRM System for Multimedia Contents over Multuple Portable Device Platforms”, 2014
[32] Hsiao-Ping Huang, “TW K-12 Academy: A Finger-driven Interact Learning System with Smart-pen Assistance on iOS Devices”, 2016
[33] Wei-Ting Chen, “Toward Smart Self-Driven Learning Environment: integrating DRM System in K12 Academy”, 2017
[34] Guozhen An, Mehrnoosh Shafiee, Davood Shamsi, “Improving Retrieval Modeling Using Cross Convolution Networks And Multi Frequency Word Embedding”, arXiv:1802.05373v2 [cs.CL] 16 Feb 2018
[35] Zhou Yu, Alexandros Papangelis, and Alexander Rudnicky. 2015. “TickTock: A non-goal-oriented multimodal dialog system with engagement awareness.” In Proceedings of the AAAI Spring Symposium.
[36] Pizza Hut Messenger BOT [online]. https://www.messenger.com/t/PizzaHutPak
[37] CNN News messenger BOT [online]. https://www.facebook.com/messages/t/cnn
[38] Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, Laszlo Lukacs, Marina Ganea, Peter Young, Vivek Ramavajjala, “Smart Reply: Automated Response Suggestion for Email”, arXiv:1606.04870v1 [cs.CL]
[39] Maria das Gracas Bruno Marietto, Rafael Varago de Aguiar, Gislene de Oliveira Barbosa, Wagner Tanaka Botelho, Edson Pimentel, Robson dos Santos Franca, Vera Lucia da Silva, “Artificial Intelligence MArkup Language: A Brief Tutorial”, arXiv:1307.3091v1 [cs.AI]
[40] Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014.” Sequence to sequence learning with neural networks. In Advances in neural information processing systems.”, 3104–3112.
[41] Tianran Hu, Anbang Xu, Zhe Liu, Quanzeng You, Yufan Guo, Vibha Sinha, Jiebo Luo, Rama Akkiraju, “Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media”, arXiv:1803.02952v2 [cs.HC]
[42] Aniruddha Tammewar, Monik Pamecha, Chirag Jain, Apurva Nagvenkar, Krupal Modi, “Production Ready Chatbots: Generate if not Retrieve”, arXiv:1711.09684v1 [cs.CL] 27 Nov 2017
[43] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean, “Distributed Representations of Words and Phrases and their Compositionality”, arXiv:1310.4546v1 [cs.CL] 16 Oct 2013
[44] M. Nadrljanski, ?. Vuki?, D. Nadrljanski, “Multi-agent systems in e-learning”, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
[45] Icons in Fig 3 are made by Good Ware, Alfredo Hernandez, Smashicons from http://www.flaticon.com/
[46] Microsoft Bot Framework [online]. https://dev.botframework.com/
[47] Chatfuel[online]. https://dev.botframework.com/
[48] Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le, ” QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension”, arXiv:1804.09541v1 [cs.CL] 23 Apr 2018, Published as a conference paper at ICLR 2018
[49] SQuAD[online]. https://rajpurkar.github.io/SQuAD-explorer/
[50] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang, “SQuAD: 100,000+ Questions for Machine Comprehension of Text”, arXiv:1606.05250v3 [cs.CL] 11 Oct 2016
[51] Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, Ming Zhou, “
Reinforced Mnemonic Reader for Machine Reading Comprehension”, arXiv:1705.02798v5 [cs.CL] 29 Apr 2018
[52] Xiaodong Liu, Yelong Shen, Kevin Duh, Jianfeng Gao, “StochasticAnswer Networks for Machine Reading Comprehension”, arXiv:1712.03556v2 [cs.CL] 15 May 2018
[53] Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, Weizhu Chen, “FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension”, arXiv:1711.07341v2 [cs.CL] 4 Feb 2018
[54] Shimi Salant, Jonathan Berant, “Contextualized Word Representations for Reading Comprehension”, arXiv:1712.03609v2 [cs.CL] 10 Mar 2018
[55] Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi, “Bidirectional Attention Flow for Machine Comprehension”, arXiv:1611.01603v5 [cs.CL] 24 Feb 2017
[56] Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes, “Reading Wikipedia to Answer Open-Domain Questions”, arXiv:1704.00051v2 [cs.CL] 28 Apr 2017
[57] Rui Liu, Junjie Hu, Wei Wei, Zi Yang, Eric Nyberg, ” Structural Embedding of Syntactic Trees for Machine Comprehension”, arXiv:1703.00572v3 [cs.CL] 31 Aug 2017
[58] Dirk Weissenborn, Georg Wiese, Laura Seiffe, “Making Neural QA as Simple as Possible but not Simpler”, arXiv:1703.04816v3 [cs.CL] 8 Jun 2017
[59] Shuohang Wang, Jing Jiang, “Machine Comprehension Using Match-LSTM and Answer Pointer”, arXiv:1608.07905v2 [cs.CL] 7 Nov 2016
[60] Andrija Bernik, Goran Buba?, Danijel Rado?evi?, “Measurement of the effects of e-learning courses gamification on motivation and satisfaction of students”, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
[61] Jean Chagas Vaz, Paul Oh, “Transformer-DARwIn: A miniature humanoid for potential robot companion”, 2018 IEEE International Conference on Consumer Electronics (ICCE)
[62] Pepper robot [online]. https://www.softbankrobotics.com/emea/en/robots/pepper
[63] Icon on Fig 2 are made by Freepik, Nikita Golubev, Icon Works from http://www.flaticon.com/
[64] Blanka Frydrychova Klimova*, Petra Poulova “Tutor as an important e-learning support” Procedia Computer Science 3 (2011) 1485–1489

指導教授

吳曉光(Hsiao-kuang Wu)

審核日期

2018-7-26

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