Multi-Task Label Embedding for Text Classification（MTLE）（ACL-2018）
论文名字：Multi-Task Label Embedding for Text Classification 动机 文本分类中的多任务学习利用相关任务之间的隐式关联来提取共同特征并获得性能增益。然而，以往的研究大多将每个任务的标签视为独立的、无意义的one-hot向量，导致潜在信息的丢失，使得这些模型很难联合学习三个或更多个任务。 预学习概念 文本分类是一种常见的自然语言处理任务，它试图为给定的
相关文章
- 1. Deep Learning for Extreme Multi-label Text Classification
- 2. A NEW METHOD OF REGION EMBEDDING FOR TEXT CLASSIFICATION
- 3. CNNs for Text Classification
- 4. Bag of Tricks for Efficient Text Classification
- 5. [Coling2018]SGM：Sequence Generation Model for Multi-Label Classification
- 6. Week1.3 Simple deep learning for text classification
- 7. Very Deep Convolutional Networks for Text Classification
- 8. Recurrent Convolutional Neural Networks for Text Classification
- 9. Orderless Recurrent Models for Multi-label Classification (CVPR2020)
- 10. 《Character-level Convolutional Networks for Text Classification》
- 更多相关文章...
- • ASP.NET Label 控件 - ASP.NET 教程
- • SVG
- SVG 教程 - • 为了进字节跳动，我精选了29道Java经典算法题，带详细讲解
- • Kotlin学习（一）基本语法
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Deep Learning for Extreme Multi-label Text Classification
- 2. A NEW METHOD OF REGION EMBEDDING FOR TEXT CLASSIFICATION
- 3. CNNs for Text Classification
- 4. Bag of Tricks for Efficient Text Classification
- 5. [Coling2018]SGM：Sequence Generation Model for Multi-Label Classification
- 6. Week1.3 Simple deep learning for text classification
- 7. Very Deep Convolutional Networks for Text Classification
- 8. Recurrent Convolutional Neural Networks for Text Classification
- 9. Orderless Recurrent Models for Multi-label Classification (CVPR2020)
- 10. 《Character-level Convolutional Networks for Text Classification》