Matching the Blanks: Distributional Similarity for Relation Learning
主要是两个contribution 对照试验：证明了在RE里面BERT使用entity marker 和 entity start的效果是最好的 训练方法：提出了一种和原始BERT类似的自监督任务训练模型，并且构造了对应的数据集 1. introduction 主要把当前的RE分为三类： （distant）supervise：让模型学习一个映射 surface form：用浅层的表示来替代一种关系
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相关文章
- 1. Matching the Blanks: Distributional Similarity for Relation Learning论文笔记
- 2. 论文笔记《Matching the Blanks: Distributional Similarity for Relation Learning》
- 3. 【论文笔记】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. 论文阅读笔记：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. 论文阅读笔记《Learning to Compare: Relation Network for Few-Shot Learning》