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研究生:蕭雅方
研究生(外文):Hsiao, Ya-Fang
論文名稱:以有限配對資料訓練事實問題生成模型之研究
論文名稱(外文):Fatual Question Generation Model Construction With Limited Paired Training Data
指導教授:柯佳伶柯佳伶引用關係
指導教授(外文):Koh, Jia-Ling
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:63
中文關鍵詞:問題生成深度學習自然語言處理語言模型遷移學習
外文關鍵詞:Question GenerationDeep LearningNatural Language ProcessingLanguage ModelTransfer Learning
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本論文考慮在閱讀文句與對應問題的配對資料有限情況下,透過遷移式學習概念,利用未配對的資料增強編碼器-解碼器架構模型的學習效果,使模型仍能生成相當於輸入大量配對資料訓練後的生成效果。本研究採用序列對序列模型,先以非監督式學習方式,利用大量無需經過標記的文句和問題,訓練自動編碼器架構。接著,擷取出預訓練好能理解文句的編碼器及生成問題的解碼器進行組合,並對編碼器加入轉移層建構出新的模型,再以遷移式學習選用文句與問題配對訓練微調模型參數。實驗結果顯示,採用本論文設計的遷移式學習方式,並配合訓練策略,在減少一半文句與問題配對資料的訓練,仍比直接採用全部配對訓練資料進行訓練得到的問題生成模型有更佳效果。
In real applications, there is usually not a large number of sentence and question pairs for training a question generation model. To solve the problem, we adopt the network-based transfer learning by using unpaired training data to enhance the learning effect of the encoder-decoder model. Accordingly, the obtained model still achieves the similar generation effect by comparing with the model which is directly trained by a large amount of paired data. In this study, we using a large number of sentences and questions that do not need to be labeled as pairs to train two auto-encoders, respectively. Then we combine the pre-trained encoder which encodes the semantics of sentence and the pre-trained decoder which generates the question. Next, by inserting a transfer layer to the encoder and fine-tune the model parameters by a fewer number of paired data. The results of experiments show that, by applying the designed training strategies, the question generation model trained by less than half of the paired training data still achieves a better performance than the model directly trained by using all the training data.
摘要 i
Abstract ii
目錄 iii
附表目錄 v
附圖目錄 vii

第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究範圍與限制 4
1.3 研究方法 5
1.4 論文架構 6

第二章 文獻探討 7
2.1 問題生成模型 (Question Generation Model) 7
2.1.1 類神經網路問題生成模型 8
2.1.2 自然語言生成技術 11
2.2 遷移式學習 (Transfer Learning) 12
2.3 注意力機制 (Attention Mechanism) 14

第三章 問題定義與系統架構 17
3.1 問題定義 17
3.2 系統架構 18
3.2.1 編碼器 (Encoder) 19
(一) 採用遞迴類神經網路模型之編碼器元件 (Encoder GRU) 19
(二) 採用 BERT 模型之編碼器元件 (Encoder BERT) 23
3.2.2 解碼器 (Decoder GRU) 26
3.2.3 擴展編碼器(Extented Encoder) 30

第四章 問題生成模型訓練方法 31
4.1 資料前處理 31
4.1.1 字詞斷詞 (Word Segmentation) 31
4.1.2 輸入編碼 (Input Encoding) 32
4.1.3 輸入嵌入 (Input Embedding) 33
4.1.4 零填充處理 (Zero Padding) 33
4.2 預訓練模型訓練 34
4.3 遷移式模型訓練 38

第五章 實驗結果與探討 40
5.1 資料集來源與參數設定 41
5.2 評估指標 43
(一) BLEU (Bilingual Evaluation Understudy) 43
(二) ROUGE(Recall-Oriented Understudy for Gisting Evaluation) 44
5.3 實驗結果與討論 45
5.3.1 採用不同編碼器元件的效果評估 45
5.3.2 採用遷移式學習的效果評估 46
5.3.3 加入轉移層的效果評估 48
5.3.4 採用擴展編碼器之效果評估 52
5.3.5 選用配對資料集數量的評估效果 54

第六章 結論與未來研究方向 59

參考文獻 60
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