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Author: 高紫瑄
Tzu-Hsuan Kao
Thesis Title: 生成式聊天機器人於古物交易客服系統之應用
The usage of generative model chatbot in antiquities trading customer service system
Advisor: 陳正綱
Cheng-Kang Chen
Committee: 賴源正
Yuan-Cheng Lai
Shi-cho Cha
Degree: 碩士
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2020
Graduation Academic Year: 108
Language: 中文
Pages: 61
Keywords (in Chinese): 聊天機器人自然語言處理樣板式模型檢索式模型生成式模型對話式商務
Keywords (in other languages): Chatbot, Natural Language Processing, Rule-based model, Retrieval-based model, Generative model, Conversational Commerce
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  • 有鑒於近年來行動裝置的發展,使通訊軟體的使用率逐漸提升。聊天機器人也帶動了對話式商務的熱潮。過去有不少以任務導向的聊天機器人為顧客服務,而這些聊天機器人大多是屬於樣板式模型的架構,使用者只能詢問特定的問題,系統從資料庫抓取答案回覆給使用者,使用者得到的回應都是制式化的回答,且因架構缺少靈活度,使用者常會有得不到聊天機器人回應的結果。檢索式模型雖能在資料庫找到較適合答案輸出給使用者,但因答案也需事先存於資料庫中,透過相似度計算來尋找關鍵字進行配對,無法針對上下文進行分析,因此結果也可能導致答案配對不理想。

    In recent years, the development of mobile devices has gradually increased the use of communication software. Chatbots have also driven the development of conversational commerce. In the past, there were a lot of task-oriented chatbots doing customer service. Most of these chatbots belong to the rule-based model. Users can only ask some specific questions, so the system will match the answer which were already created in databased, and then reply to the user. The response that the user received is all standardized answer. Also, because of the system architecture lacks flexibility, users often fail to get their response from the chatbot. Although the retrieval model can find a more suitable answer in the database, but the answer also needs to be stored in the database in advance, through similarity calculation to find keywords to match, it is impossible to analyze the context, so the answer matching is not ideal.
    This thesis will discuss the technology of chatbots, mainly in the field of antiquities. Build a chatbot based on a generative model, so that the chatbot can analyze the context of the whole sentence and automatically generate a reply to solve this problem. Using the third-party dialogue dataset and the antiquities field dataset created in this article. These data are processed in natural language, and the text is converted into vectors for model training. The model evaluation proves that the score of generative model is higher than retrieval model. The chatbot process designed in this paper can ensure that users get the most relevant answers when they ask professional questions in the field of antiquities.

    摘要 I ABSTRACT II 致謝 III 目錄 IV 圖目錄 VI 表目錄 VII 第一章 簡介 1 1.1 研究背景 1 1.2 研究動機 4 1.3 研究目的 5 1.4 研究架構 6 第二章 文獻探討 7 2.1 聊天機器人簡介 7 2.2 聊天機器人特性 8 2.3 聊天機器人類型與應用場景 9 2.4 聊天機器人系統架構 11 2.5 聊天機器人技術原理 13 2.5.1 樣板式模型 (Rule-based model) 13 2.5.2 檢索式模型 (Retrieval-based model) 15 2.5.3 生成式模型 (Generative model) 18 2.5.4 聊天機器人三種模型比較 21 2.6 古物領域相關研究 23 2.6.1 古物領域簡介 23 2.6.2 古物商販售與收購流程 24 第三章 生成式聊天機器人研究模型 27 3.1 預處理 30 3.2 模型評測 31 第四章 聊天機器人設置與實驗分析 33 4.1 生成式聊天機器人研究設置 34 4.1.1 第三方資料集與古物資料集簡介 34 4.1.2 斷詞與詞向量 35 4.1.3 模型訓練 38 4.1.4 參數設定 38 4.2 實驗結果 39 第五章 結論 43 5.1 研究結論 43 5.2 實務貢獻 44 5.3 研究限制與未來方向 44 參考文獻 46 附錄一 50

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