机器阅读理解 | (4) QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization
本篇博客中我将分享一篇EMNLP2019与文本问答系统鲁棒性相关的论文：《QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization》。 论文下载连接 开源代码(添加注释;提取码:nq7f) 摘要 标准的准确率指标表明现在的阅读理解系统在很多数据集上都取得了很好的表现。然而，现有系统
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相关文章
- 1. DIM（Learning deep representations by mutual information estimation and maximization）
- 2. 【论文笔记——DIM】Learning Deep Representations By Mutual Information Estimation and Maximization
- 3. Cycle-Consistency for Robust Visual Question Answering阅读笔记
- 4. 论文阅读 Question Answering
- 5. nips 208 visual question answering 导读
- 6. 论文阅读笔记《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 7. More Accurate Question Answering on Freebase阅读笔记
- 8. Mutual information and Normalized Mutual information
- 9. 机器阅读理解 | (3) 机器阅读理解简述
- 10. 文献阅读笔记：Unsupervised Question Answering by Cloze Translation