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論文名稱(外文):Designing Situation-Based English Learning with a Dialogue Robot
外文關鍵詞:natural language processingrobot-assisted language learningintent recognitionentity recognitiondeep learning
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同時訓練閱讀及聽力,進而達到提升英文能力。系統架構分為 APP 端、自然語言
行預訓練,5 種模型如下: GPT2、BERT、DistilBERT、XLNet、RoBERTa,分別觀
察這五種模型的 Intent loss、Entity loss 以及正確率的表現狀況。經過實驗發現,意
法準確預測。經過實驗之後發現使用 DistilBERT 進行預訓練比其他四個模型精確
平均度還高,替整個文本意圖預測成功平均提升至 99.59%以上。

In recent years, Taiwan's English proficiency has been declining year by year in nonnative English-speaking countries. The main reason is that most students rarely actively
learn English and feel anxious about learning English. This research proposes a
situational English dialogue robot system that applies natural language processing to
robots, allowing students to practice English dialogue while interacting with virtual and
physical robots, eliminating the tension caused by students having English conversations
with humans It can not only train children to speak English, but also train reading and
listening at the same time, so as to improve their English ability. The system architecture
is divided into an APP side, a natural language processing side, a web page display side,
and a robot side, and contextual English conversations are conducted through pictures
and texts. However, in the past, language bots suffered from low accuracy of language
modules and too many errors in intent and entity extraction during training. In order to
solve the above problems, this study uses an open source learning framework-Rasa, and
uses five language models for pre-training. The five models are as follows: GPT2, BERT,
DistilBERT, XLNet, RoBERTa, and observe the Intent loss, Entity loss and the
performance of the correct rate. Through experiments, it is found that the main reason for
the excessive loss of intents and entities is that there are too many marked entities and
unmarked entities in one intent, which will cause the entities in the corpus model and the
marked entities to conflict with the language model entities. cannot be predicted
accurately. After experiments, it is found that using DistilBERT for pre-training is more
accurate than the other four models, and the success of the entire text intent prediction is
improved to more than 99.59% on average.
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 7
3.1. 應用程式端(APP) 7
3.1.1. Android Studio 7
3.1.2. Java 語言 7
3.1.3. 語音辨識 8
3.1.4. 硬體規格介紹 8
3.2. 自然語言處理端 9
3.2.1. Rasa背景介紹 9
3.2.2. Rasa目錄結構介紹 9
3.3. 伺服器後端 13
3.3.1. Python語言 13
3.3.2. Flask框架 13
3.3.3. MySQL語言 13
3.4. 網頁顯示端 15
3.4.1. HTML語言 15
3.4.2. JavaScript語言 15
3.4.3. jQuery庫 15
3.4.4. CSS語言 15
3.5. 機器人端 17
3.5.1. ROS框架 17
3.5.2. Yolo V3 17
3.5.3. OpenCV 17
3.5.4. DJI RobotMaster EP SDK 18
3.5.5. 硬體規格介紹 18
第四章 系統架構與研究方法 20
4.1. 系統框架 20
4.2. 應用程式端流程圖 21
4.3. 後端&RASA伺服器 22
4.4. 自然語言理解 23
4.4.1. Rasa NLU 23
4.4.2. 分詞器-WhitespaceTokenizer 24
4.4.3. 特徵化器 24
4.4.4. 稀疏特徵化器-CountVectorizerFeaturizers 24
4.4.5. 密集特徵化器-LanguageModelFeaturizers 25
4.4.6. BERT 27
4.4.7. GPT-2 30
4.4.8. XLNet 34
4.4.9. DistilBERT 39
4.4.10. RoBERTa 41
4.4.11. 意圖&實體分類器-DIETClassifiers 44
4.5. 對話管理 47
4.5.1. Rasa Core 47
4.5.2. Rasa Core策略訓練 49
4.6. 解決意圖與實體預測錯誤過高的問題以及實驗流程 51
4.7. 網頁顯示端 53
4.8. 設計聊天機器人實驗流程 58
4.9. 模型訓練資訊與電腦規格 59
第五章 研究結果與分析 60
5.1. 針對意圖與實體結果與分析 60
5.1.1. BERT (No NER)比較BERT (NER) 62
5.1.2. DistilBERT (No NER)比較DistilBERT (NER) 64
5.1.3. GPT2 (No NER)比較GPT2 (NER) 66
5.1.4. RoBERTa (No NER)比較RoBERTa (NER) 68
5.1.5. XLNet (No NER)比較XLNet (NER) 70
XLNet (NER) 70
5.1.6. 五種模型的實體與意圖F1-Score分析 72
5.1.7. 無模型與有模型之學習影響分析 74
5.2. 針對有無提升英文口說能力之分析 77
5.3. RASA弱點分析&自動化NER相似詞彙 82
5.4.保留無實體(NO NER)與刪除無實體(NER)詞彙模型訓練比較 86
5.5. 論文比較 89
第六章 結論與未來展望 91
6.1. 結論 91
6.2. 未來展望 91
參考文獻 93
附錄 97
附錄一、整體故事情節 97
附錄二、PARK故事情節 98
附錄三、SUPERMARKET故事情節 99
附錄四、RESTAURANT故事情節 100
附錄五、SCHOOL故事情節 101
附錄六、LIBARAY故事情節 102
附錄七、BERT(NO NER)與BERT(NER)混淆矩陣比較 103
附錄九、GPT2(NO NER)與GPT2 (NER)混淆矩陣比較 105
附錄十一、ROBERTA(NO NER)與ROBERTA (NER)混淆矩陣比較 107

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