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。;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.
