Text Understanding with the Attention Sum Reader Network
關鍵詞 Bi-GRU, Bi-LSTM, attention sum 來源 arXiv 2016.03.04 (published at ACL 2016) 問題 使用帶attention的深度模型解決完型填空問題 技術細節 模型比attentive reader簡單，分以下幾步： 使用雙向GRU/LSTM單元計算docment每個詞的拼接詞向量doc_endcoer 使用雙向GRU/LSTM單元
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相關文章
- 1. Text Understanding with the Attention Sum Reader Network
- 2. 《 Text Understanding with the Attention Sum Reader Network 》——Attention Sum Reader
- 3. 論文《Text Understanding with the Attention Sum Reader Network》筆記
- 4. 【論文筆記02】Text Understanding with the Attention Sum Reader Network
- 5. 【論文筆】AS Reader：Text Understanding with the Attention Sum Reader Network
- 6. Understanding Safari Reader
- 7. Understanding SIP with Network Address Translation (NAT)
- 8. Translation with a Sequence to Sequence Network and Attention
- 9. text to image（一）:《GENERATING IMAGES FROM CAPTIONS WITH ATTENTION》
- 10. SaGAN:Generative Adversarial Network with Spatial Attention for Face Attribute Editing