中文電影對話問答系統資料集

Chinese Movie Dialogue Question Answering Dataset

本論文建構一個中文對話式問答資料集CMDQA。內容為中文電影資訊獲取的多輪對話場景，總共包含一萬筆對話，共約四萬輪對話。所有問題與背景文檔，皆由網路爬蟲從維基百科彙整而來。每個問題的答案都是其相關文檔內的某個片段。此外，為了模擬真實對話問答的情景，對話中會有代名詞的使用。因此，在CMDQA中，問答模型除了需自動地檢索相關文檔外，亦需處理代名詞與歷史資訊的問題。除了對話式多輪問答外，本資料集還可用於評估資訊檢索、機器閱讀理解與問題轉寫等任務的模型成效。除了CMDQA以外，本研究提供一個基礎系統並測試其效果。實驗顯示，基礎系統的效能與真人尚有相當大的差異，因此本資料集可對相關研究提供足夠的挑戰性。

This paper constructs a Chinese dialogue-based information-seeking question answering dataset CMDQA, which is mainly applied to the scenario of getting Chinese movie related information. It contains 10K QA dialogs (40K turns in total). All questions and background documents are compiled from the Wikipedia via an Internet crawler. The answers to the questions are obtained via extracting the corresponding answer spans within the related text passage. In CMDQA, in addition to searching related documents, pronouns are also added to the question to better mimic the real dialog scenario. This dataset can test the individual performance of the information retrieval, the question answering and the question re-writing modules. This paper also provides a baseline system and shows its performance on this dataset. The experiments elucidate that it still has a big gap to catch the human performance. This dataset thus provides enough challenge for the researcher to conduct related research.