Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference
基于连词加强网络和强化学习的自然语言推理模型 这篇文章是浙江大学CAD&CG国家重点实验室和阿里巴巴-浙大前沿技术联合研究院合作设计了基于连词加强网络的自然语言推理方法，并在此基础上应用了强化学习来整合不同样本的标注意见不统一的情况，从而提升模型的稳定性。该成果已发表于自然语言处理领域国际顶级会议ACL 2018。最近看到这篇文章感到对自己启发挺大，所以写个微博记录一下~ 摘要 自然语言推理（Na
相关文章
- 1. Deep Reinforcement Learning with a Natural Language Action Space
- 2. Enhanced LSTM for Natural Language Inference
- 3. [ACL2017]Enhanced LSTM for Natural Language Inference
- 4. CS224d: Deep Learning for Natural Language Process
- 5. 《Enhanced LSTM for Natural Language Inference》论文总结
- 6. Natural Language Processing[论文合集]
- 7. Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
- 8. DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
- 9. 【笔记】CS224N Natural Language Processing with Deep Learning
- 10. Stochastic Answer Networks for Natural Language Inference阅读笔记
- 更多相关文章...
- • RSS
元素 - RSS 教程 - • XSL-FO marker 对象 - XSL-FO 教程
- • Java Agent入门实战（三）-JVM Attach原理与使用
- • YAML 入门教程
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。
最新文章
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Deep Reinforcement Learning with a Natural Language Action Space
- 2. Enhanced LSTM for Natural Language Inference
- 3. [ACL2017]Enhanced LSTM for Natural Language Inference
- 4. CS224d: Deep Learning for Natural Language Process
- 5. 《Enhanced LSTM for Natural Language Inference》论文总结
- 6. Natural Language Processing[论文合集]
- 7. Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
- 8. DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
- 9. 【笔记】CS224N Natural Language Processing with Deep Learning
- 10. Stochastic Answer Networks for Natural Language Inference阅读笔记