博碩士論文 105552014 詳細資訊




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姓名 郭敏楓(Min-Feng Kuo)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 對話系統應用於中文線上客服助理:以電信領域為例
(Dialogue system applied to Chinese online customer service assistant:a case study of telecom-domain)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2022-7-24以後開放)
摘要(中) 在人工智慧領域發展快速的時代中,我們一直在尋找機器可以幫助人類的方式。在自然語言領域中,智慧對話機器人是近年來備受重視的項目。然而教導機器要如何與人類溝通,以完成一項具體任務是相當有挑戰性的。其中一個需要克服的問題在於學術研究上常預先定義出特定領域中對話的明確特性,再依照這些明確的定義去設計對話情境並蒐集對話資料集,蒐集的過程中會針對句中的意圖及目標詞彙與其類別進行標注,也會額外標出該次對話中的一些細節(如:限制條件、最終目標、是否完成對話任務…等)。這些額外標注的資訊是現實對話資料中不會出現的。使用這種方式蒐集的資料,有許多跟實際狀況不同的地方。
本研究主要是探討中文客服資料集要如何在對話系統中訓練各個模組,並舉出理想資料集與實際資料集的差異。在中文對話系統裡的自然語言理解模組,需要對自然語句做斷詞及命名實體辨識。我們改進了斷詞模組及命名實體辨識模組,使其效能提升並增加可擴充性。使用改進後的中文對話理解模組進行訓練深度學習模型,相比一般的自然語言理解模組,在中文客服對話系統的效果有所提升。
摘要(英) In the era of artificial intelligence field rapidly develop, we are always searching for ways in which machines can help the human.
And in the field of natural language processing, the intelligent conversational bot is the project that has been highly regarded in recent years.
However, it is full of challenges to teach machines how to communicate with humans in order to accomplish a specific task. One of the problems we need to overcome is that during academic research, we often define the explicit characteristic in dialogues of specific field beforehand, then design the situation of the dialogue and collect the dialogue data set. While collecting the data set, intentions lie in the sentences, target vocabulary and its category would be marked. Furthermore, details in the certain conversation such as condition restriction, the ultimate goal, and whether it accomplishes the conversation task or not, would also be marked. This additional information we mark would not appear in real conversation data, hence the data collected this way would be different from actual circumstances.
This study is mainly to explore how the Chinese customer service dialogue dataset trains each module in the dialogue system and list out the differences between ideal and actual datasets.
In the Chinese dialogue system, the natural language understanding module needs to do the word segment and the Named-Entity Recognition. We improve the Chinese Word Segment module and the Named-Entity Recognition so as to upgrade the efficiency and increase the expansibility. The improved Chinese language understanding module is used to enhance the training deep learning model. Compare to the ordinary natural language understanding module, the effectiveness has been improved.
關鍵字(中) ★ 自然語言處理
★ 對話系統
★ 中文斷詞
★ 命名實體辨識
★ 卷積類神經網路
★ 長短期記憶模型
關鍵字(英) ★ Natural Language Processing
★ Dialogue System
★ Chinese Word Segment
★ Named-Entity Recognition
★ Convolution Neural Network
★ Long Short-Term Memory
論文目次 摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 章節概要 3
第二章 文獻探討 4
2.1 對話系統 4
2.1.1自然語言理解 5
2.1.2對話管理 7
2.1.3自然語言生成 7
2.2 深度學習 8
2.2.1 卷積神經網路(Convolutional Neural Network) 8
2.2.2 循環神經網路(Recurrent Neural Network) 9
2.2.3 長短期記憶網路(Long short-term memory) 10
2.2.4 雙向循環神經網路(Bi-directional Recurrent Neural Network) 12
第三章 實驗資料分析 14
3.1 實驗資料分析 14
第四章 研究方法 19
4.1自然語言理解 19
4.1.1資料清理模組 19
4.1.2命名實體辨識模組 20
4.1.3中文斷詞模組 21
4.2 深度神經網路模型框架 (DEEP NEURAL NETWORK, DNN) 22
4.2.1句子編碼器 (Sentence Encoder) 23
第五章 實驗方法 25
5.1參數說明 25
5.2實驗結果 26
5.2.1 命名實體辨識模型 26
5.2.2 斷詞模型 27
5.2.3 對話系統模型結果 28
5.3錯誤分析 29
第六章 結論與未來研究 31
6.1結論 31
6.2未來方向 31
附錄 33
參考文獻 35
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指導教授 蔡宗翰(Tzong-Han Tsai) 審核日期 2018-7-26
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