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研究生:胡雅婷
研究生(外文):HU, YA-YING
論文名稱:輔助孩童學習英語之深度學習對話機器人
論文名稱(外文):A Dialog Robot with Deep Learning Model for Children Learning English
指導教授:黃永廣黃永廣引用關係
指導教授(外文):WONG, WING-KWONG
口試委員:謝文敏陳凱萍
口試委員(外文):HSIEH, WEN-MINCHEN, KAI-PING
口試日期:2021-07-06
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:84
中文關鍵詞:自然語言處理機器人輔助語言學習深度學習意圖識別插槽填充
外文關鍵詞:natural language processingrobot-assisted language learningdeep learningintent recognitionslot filling
相關次數:
  • 被引用被引用:1
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  • 下載下載:83
  • 收藏至我的研究室書目清單書目收藏:1
在傳統的課堂學習中,學生都是從課本上閱讀,很少進行對話練習。本研究提出一個自然語言處理之英語對話互動機器人系統,可以讓學生進行英語對話練習。系統架構分為APP端、自然語言處理端及網頁顯示端,APP端設計為教學及對話兩種模式,以圖文並茂方式進行對話情境來學習英語口說能力。而過去對話機器人,在於語音辨識錯誤時缺乏理解學生語句之能力,且無過去記憶功能。本研究為解決以上問題,使用一套開源學習框架-Rasa,利用兩種方式的分類器進行深度學習模型訓練,分別是DIET Classifier以及DIET Classifier with BERT,經過實驗,選擇在識別意圖、實體、插槽填充和對話管理方面有最佳性能,並具有 99% 準確率的DIET Classifier with BERT分類器,藉由插槽填充功能,機器人可以記住當前對話的內容並預測下一步動作。在未來的工作中,將使用擁有抓取物體的機械手臂之物理移動機器人代替模擬機器人,使學習者有更深層次的互動和參與感。
In traditional classroom learning, students read from textbook with little practice of conversation. This research proposes an English dialogue interactive robot with deep learning for natural language processing that can engage students in conversation. The system architecture is composed of an APP on a mobile device, a natural language processing backend and a web page frontend. APP works in a training or a practice mode in a dialogue context with both pictures and texts. Traditional robots do not know how to respond when the learner’s voice cannot be recognized, and generally have no memory of previous dialog. To address these issues, this research employs open source Rasa framework, and two types of classifiers for deep learning training, namely DIET Classifier and DIET Classifier with BERT for performance comparison. After experimentation, the DIET Classifier with BERT model with 99% accuracy is selected for its optimal performance in recognizing intents, entities, filling slots and managing dialogue. With the ability of slot filling, the robot can memorize the content of the current dialogue and predict the next action. In future work, a physical mobile robot with a manipulator for grabbing objects will be used instead of the simulated robot, so that the learner can have a deeper sense of physical interaction and participation.
摘要 i
ABSTRACT ii
誌謝 iii
目錄 v
表目錄 viii
圖目錄 ix
第一章、緒論 1
1.1. 研究背景 1
1.2. 研究動機與目的 2
第二章、文獻探討 3
第三章、系統與工具介紹 5
3.1. APP端 5
3.1.1. Xcode 5
3.1.2. Swift語言 5
3.1.3. 語音辨識 6
3.1.4. 硬體規格介紹 6
3.2. 自然語言處理端(後端伺服器) 7
3.2.1. Python語言 7
3.2.2. Flask 7
3.2.3. Rasa框架 8
3.2.4. MySQL 9
3.2.5. NLTK套件 9
3.3. 網頁顯示端 10
3.3.1. JavaScript語言 10
3.3.2. HTML語言 11
3.3.3. CSS 11
3.3.4. Socket 12
3.3.5. Node.js 12
第四章、系統架構及研究方法 13
4.1. 系統架構 13
4.2. APP端流程圖- 教學模式 14
4.3. APP端流程圖- 對話模式 17
4.4. 後端伺服器 19
4.5. 自然語言處理-預處理 20
4.6. 自然語言理解 21
4.6.1. Rasa NLU 21
4.6.2. DIET Classifier 介紹 22
4.6.3. DIET Classifier模型架構 25
4.6.4. BERT介紹 30
4.6.5. DIET Classifier with BERT模型架構 33
4.7. 對話管理 35
4.7.1. Rasa Core介紹 35
4.7.2. Rasa Core訓練架構 36
4.7.3. 追蹤器 41
4.7.4. 解決語音辨識錯誤時的誤判 43
4.8. 模型訓練資訊 44
4.9. 前端網頁顯示端 45
第五章、研究結果與分析 49
5.1. 模型訓練之比較 49
5.2. 針對意圖結果與分析 52
5.3. 針對對話模型結果與分析 55
5.4. 論文比較 56
第六章、結論與未來展望 59
6.1. 結論 59
6.2. 未來展望 59
參考文獻 60
附錄 66


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