機器閱讀理解 | (4) QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization
本篇博客中我將分享一篇EMNLP2019與文本問答系統魯棒性相關的論文：《QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization》。 論文下載連接 開源代碼(添加註釋;提取碼:nq7f) 摘要 標準的準確率指標表明現在的閱讀理解系統在很多數據集上都取得了很好的表現。然而，現有系統
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相關文章
- 1. DIM（Learning deep representations by mutual information estimation and maximization）
- 2. 【論文筆記——DIM】Learning Deep Representations By Mutual Information Estimation and Maximization
- 3. Cycle-Consistency for Robust Visual Question Answering閱讀筆記
- 4. 論文閱讀 Question Answering
- 5. nips 208 visual question answering 導讀
- 6. 論文閱讀筆記《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 7. More Accurate Question Answering on Freebase閱讀筆記
- 8. Mutual information and Normalized Mutual information
- 9. 機器閱讀理解 | (3) 機器閱讀理解簡述
- 10. 文獻閱讀筆記：Unsupervised Question Answering by Cloze Translation