Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
代碼位置 論文原文 研究意義 LSTM具有超時保存序列信息的優良性能，同時更復雜的計算單元，因此在衆多的序列任務中取得了很好的效果，僅僅基於LSTM結構至今仍然是一個線性鏈。然而，自然語言表現出自然地將單詞與短語組合在一起的句法性質。本文提出了樹結構的LSTM，將LSTM推廣到樹狀的網絡拓撲結構，Tree-LSTMs在預測兩個句子的語義相關性和電影評論中進行情感分類兩個方面上進行實驗，證明都要優於
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相關文章
- 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)公式簡介
- 9. [ACL2016]Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
- 10. Universally Slimmable Networks and Improved Training Techniques_ICCV_2019