論文分享 - Reinforced Mnemonic Reader for Machine Comprehension
簡介 這篇論文發表時間比較近，比較全面地總結了match-LSTM、R-Net等衆多前人模型的優缺點，並做了很好的改進，如：增加編碼層能力，解決長距離上下文信息，提煉預測答案片段，直接優化評價函數等，在SQuAD數據庫上取得了State-Of-Art的效果。 前人的很多模型都具有一個共同的網絡框架，即「encoder-interaction-pointer」。首先是將問題和段落的單詞序列利用RNN
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相關文章
- 1. 論文分享 - R-Net: Machine Reading Comprehension with Self-Matching
- 2. 論文《Adversarial Reading Networks For Machine Comprehension》
- 3. 論文分享 - Machine Comprehension Using Match-LSTM and Answer Pointe
- 4. 論文分享 - Machine Comprehension Using Match-LSTM and Answer Pointer
- 5. 論文分享 - Reasoning with Memory Augmented Neural Networks for Language Comprehension
- 6. Bi-Directional Attention Flow For Machine Comprehension
- 7. BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION
- 8. Bidirectional Attention Flow for Machine Comprehension
- 9. 論文筆記《Gated-Attention Readers for Text Comprehension》
- 10. 論文閱讀|Cross-Lingual Machine Reading Comprehension