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:108 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。
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