核心思想
基于阅读理解中QA系统的样本中可能混有对抗样本的情况，在寻找答案时，首先筛选出可能包含答案的句子，再做进一步推断。
方法
Part 1
given: 段落C query Q
段落切分成句子：
每个句子和Q合并：
使用依存句法分析得到表示：
基于T Si T Q ，分别构建 Tree-LSTMTree-LSTM
两个Tree-LSTMs的叶结点的输入都是GloVe word vectors
输出隐向量分别是 h h
h h连接起来并传递给一个前馈神经网络来计算出Si包含Q的答案的可能性
loss 和前馈神经网络follows语义相关性网络
有监督的训练时，si包含答案为1，否则为0。
Part 2
计算最可能答案：
L代表QA似然神经网络预测的似然
将一对句子S*和Q传递给预先训练好的单BiDAF(Seo et al.， 2016)，生成Q的答案a^。
实验
数据集：sampled from the training set of SQuAD v1.1
there are 87,599 queries of 18,896 paragraphs in the training set of SQuAD v1.1. While each query refers to one paragraph, a paragraph may refer to multiple queries.
d=87,599 is the number of queries. The set D contains 440,135 sentence pairs, among which 87,306 are positive instances and 352,829 are negative instances.
positive instance: ，前者包含后者的答案。
两种采样方法： pair-level sampling ，paragraph-level sampling
1. In pair-level sampling, 45,000 positive instances and 45,000 negative instances are randomly selected from D as the training set.
2. paragraph-level sampling 首先随机选Qk，然后从Dk中随机采样出一个positive instance 和一个negative instance
Each set has 90,000 instances. The validation set with 3,000 instances are sampled through these two methods as well.
测试集：ADDANY adversarial dataset : 1,000 paragraphs and each paragraph refers to only one query. By splitting and combining, 6,154 sentence pairs are obtained.
实验设置：The dimension of GloVe word vectors (Pennington et al., 2014) is set as 300. The sentence scoring neural network is trained by Adagrad (Duchi et al., 2011) with a learning rate of 0.01 and a batch size of 25. Model parameters are regularized by a 10 strength of per-minibatch L2 regularization.
结果
评价标准：Macro-averaged F1 score (Rajpurkar et al., 2016; Jia and Liang, 2017).
对于table2，可以理解为二分类问题。
consider three types of sentences: adversarial sentences, answer sentences, and the sentences that include the answers returned by the single BiDAF system.
the x-axis denotes the ranked position for each sentence according to its likelihood score , while the y-axis is the number of sentences for each type ranked at this position.
It shows that among the 1,000 (C;Q) pairs, 647 and 657 answer sentences are selected by the QA Likelihood neural network based on pair-level sampling and paragraph-level sampling respectively, but only 136 and 141 adversarial sentences are selected by the QA Likelihood neural network.
结论
对于ADDSENT的没有做。