臺灣博碩士論文加值系統

本研究透過RASA3.0版建立出自然語言理解(NLU)的模型，去實作包含機器人的對話功能及意圖識別功能，在傳統的聊天機器人中，往往都是透過固定腳本的方式去生成語句，但為了提高靈活性及有效性，因此選擇使用RASA聊天機器人進行本次研究實驗。現在許多零售企業都設有客服部門，並由專人為消費者所提出的問題進行溝通及解決，但這也是企業增加許多不必要的成本支出，因此本研究希望能透過訓練完整的聊天機器人去幫助企業節省成本的支出。而本研究將以太陽眼鏡的情境去模擬出客人在購買過程中會遇到的相關任何問題，根據消費者對產品型號的需求及產品配戴上的任何疑問，都可以透過rasa所訓練出的模型去有效的解決消費者在購買上的疑問。

This study establishes a natural language understanding (NLU) model using RASA 3.0 to implement conversational and intent recognition functionalities in a chatbot. Traditional chatbots often generate sentences using fixed scripts. However, in order to enhance flexibility and effectiveness, RASA chatbot was chosen for this research experiment. Nowadays, many retail companies have customer service departments dedicated to communicating and resolving issues raised by consumers. However, this also leads to unnecessary cost expenditures for the companies. Therefore, this research aims to train a comprehensive chatbot to help businesses save on costs. The study simulates scenarios related to purchasing sunglasses, where any relevant questions that customers may encounter during the buying process can be effectively addressed using the trained RASA。

目錄
摘要 i
ABSTRCT ii
表目錄 v
壹、緒論 1
1.1研究背景 1
1.2研究動機 2
1.3研究目的 3
1.4 論文架構與研究流程 4
貳、文獻探討 6
2.1自然語言理解(NLU) 6
2.2聊天機器人 7
2.3 Transfer Learning 9
2.4 Bert 10
2.5 Jieba 12
參、研究方法 13
3.1 Anaconda環境建置 13
3.2 rasa內部檔案設置 14
3.3太陽眼鏡情境設計 22
肆、研究結果 23
4.1系統建置流程 23
4.2運行Actions檔案內容 24
4.3情境對話成果展示 25
伍、結論與未來展望 32
5.1研究限制 32
5.2 研究貢獻 32
5.3未來研究方向 33
參考文獻 34

參考文獻
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