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研究生： 劉秝瑋
Liu, Li-Wei
論文名稱： 以句子雙重情境表示法建模改進情緒原因句配對擷取之研究
Clause Dual-Context Representation Learning for Improving End-to-End Emotion-Cause Pair Extraction
指導教授： 柯佳伶
Koh, Jia-Ling
口試委員： 陳良弼 吳宜鴻 范耀中 柯佳伶
口試日期： 2021/08/17
學位類別： 碩士
Master
系所名稱： 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2021
畢業學年度： 109
語文別： 中文
論文頁數： 55
中文關鍵詞： 情緒原因句配對擷取 、深度學習 、自然語言理解 、多任務學習
研究方法: 實驗設計法
DOI URL： http://doi.org/10.6345/NTNU202101310
論文種類： 學術論文
相關次數： 點閱：132  下載：13 
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    • 針對情緒原因句配對擷取任務，本論文提出一個基於句子雙重情境表示法建模的模型。本研究提出的模型中對文本中同一個句子分別學習情緒句及原因句情境表示法，並採用多任務學習的訓練方式，使模型在訓練時除了考慮情緒原因句配對預測任務，同時考慮情緒句及原 因句預測子任務，以學習語意更完整的情緒原因句配對表示法進行配對預測。此外，本研究考慮情緒句及原因句在文本中正負樣本數不平衡的問題，因此採用損失函數權重調整策略，使模型在訓練後能提高情緒句及原因句預測子任務的回復率，連帶提升情緒原因句配對擷取任務的預測效果。實驗結果顯示，本論文方法以兩個圖神經網路學習句子雙重情境表示法，並配合損失函數權重調整策略，較相關研究以單個圖神經網路學習情境表示法的模型，在情緒原因句配對擷取任務達到更佳的效果。

    附表目錄 ........................................................................................................................................ iv 附圖目錄 .......................................................................................................................................... v 第一章 緒論................................................................................................................................. 1 1.1 研究動機與目的 .......................................................................................................... 1 1.2 研究方法 ........................................................................................................................ 4 1.3 論文架構 ........................................................................................................................ 7 第二章 文獻探討 ....................................................................................................................... 8 2.1 情緒原因句擷取(Emotion cause extraction ,ECE) .................................... 8 2.1.1 基於規則式(Rule-Based)的情緒原因句擷取 .................................................................. 8 2.1.2 機器學習(Machine Learning)方法的情緒原因句擷取 ................................................. 9 2.1.3 深度學習(Deep Learning)方法的情緒原因句擷取 ..................................................... 10 2.2 情緒原因句配對擷取（Emotion-cause pair extraction ,ECPE）.............................. 12 2.2.1 兩步驟 two step 處理的情緒原因句配對擷取 .............................................................. 12 2.2.2 端對端(End to end)的情緒原因句配對擷取 ................................................................. 14 第三章 句子雙重情境表示法學習模型....................................................................................... 18 3.1 問題定義 .................................................................................................................................... 18 3. 2 模型架構 ................................................................................................................................... 19 3.2.1 句子的雙重情境表示法建模 .............................................................................................. 20 (1) 卷積神經網路 Convolutional Neural Network CNN) ................................................. 20 (2) 圖神經網路 Graph Neural Network GNN) ..................................................................... 21 (3) 預測情緒句及原因句的子任務 .............................................................................................. 22 3.2.2 句子配對表示法的學習與配對排序 ................................................................................. 23 3.2.3 損失函數 ................................................................................................................................. 24 第四章 實驗結果與探討 ................................................................................................................ 26 4.1 資料集來源與參數設定 .......................................................................................................... 26 4.2 評估指標及評估標準 .............................................................................................................. 28 4.2.1 評估指標的計算公式說明 .................................................. 28 4.2.2 評估的預測任務 ................................................................................................................... 29 4.3 實驗結果與討論..........................................................................................30 4.3.1 採用句子雙重情境表示法模型架構的效果評估 ............................... 30 4.3.2 在損失函數採用權重調整策略的效果評估 ........................................ ..........34 4.3.3 2-gnn模型對不同特性資料的預測效果評估.................................................. 36 4.3.4 個案實驗討論 ............................................................................................. 47 第五章 結論與未來研究方向 ...................................................................................................... 49 參考文獻 .......................................................................................................................................... 50

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