Matching the Blanks: Distributional Similarity for Relation Learning
主要是兩個contribution 對照試驗：證明了在RE裏面BERT使用entity marker 和 entity start的效果是最好的 訓練方法：提出了一種和原始BERT類似的自監督任務訓練模型，並且構造了對應的數據集 1. introduction 主要把當前的RE分爲三類： （distant）supervise：讓模型學習一個映射 surface form：用淺層的表示來替代一種關係
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相關文章
- 1. Matching the Blanks: Distributional Similarity for Relation Learning論文筆記
- 2. 論文筆記《Matching the Blanks: Distributional Similarity for Relation Learning》
- 3. 【論文筆記】Graph Matching Networks for Learning the Similarity of Graph Structured Objects
- 4. distributional similarity based representations: word2vec
- 5. Bridging the Gap Between Relevance Matching and Semantic Matching for Short Text Similarity Modeling
- 6. 論文閱讀筆記：Graph Matching Networks for Learning the Similarity of Graph Structured Objects
- 7. Similarity Metric Learning for Face Recognition2013
- 8. Multi-Label Transfer Learning for Semantic Similarity
- 9. Matching Networks for One Shot Learning
- 10. 論文閱讀筆記《Learning to Compare: Relation Network for Few-Shot Learning》