Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
代码位置 论文原文 研究意义 LSTM具有超时保存序列信息的优良性能，同时更复杂的计算单元，因此在众多的序列任务中取得了很好的效果，仅仅基于LSTM结构至今仍然是一个线性链。然而，自然语言表现出自然地将单词与短语组合在一起的句法性质。本文提出了树结构的LSTM，将LSTM推广到树状的网络拓扑结构，Tree-LSTMs在预测两个句子的语义相关性和电影评论中进行情感分类两个方面上进行实验，证明都要优于
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相关文章
- 1. Long Short Term Memory networks(LSTMs)
- 2. Sequence Models and Long-Short Term Memory Networks
- 3. Memory Networks
- 4. Long Short-Term Memory (LSTM)
- 5. 6、Long Term Load Forecasting with Hourly Predictions based on Long-Short-Term-Memory Networks
- 6. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 7. LSTM（Long Short-Term Memory）
- 8. Long Short-Term Memory (LSTM)公式简介
- 9. [ACL2016]Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
- 10. Universally Slimmable Networks and Improved Training Techniques_ICCV_2019