隨著科技的進步，智慧型裝置的使用越來越普遍，而其中，對話系統的發展也逐漸受到重視。人與人之間的對話一直都是門很深奧的學問，而人與機器之間的對話更是值得我們去深究的課題。
在一般的對話的過程中，常常會有不明確性(ambiguity)產生，但因為人類對話的內容太過廣泛，而其中產生的不明確性所涉及的層次也是相當龐大的，因此我們將研究範圍限定在對話系統中理解使用者意圖的部分。我們參考BUDS POMDP Dialogue System (Steve Young, 2012)設計了一套電影查詢系統，系統會根據與使用者的對話內容推薦符合條件的電影，系統將會分析使用者輸入的語句，透過學習的方式來推測使用者真正的意圖，再根據推測結果採取合適的行動。
我們收集了200位使用者與系統的互動的對話歷史並作成語料庫，根據語料庫的結果歸納出幾種對話過程中會產生的不明確性問題，並制定相關的判定標準以及回應方式。最後，根據實驗結果顯示我們的系統能夠正確判斷不明確性的類型並加以處理。

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