In Original ADDSENT ADDONESENT BiDAF-S (Seo et al., 2016) 75.5 34.3 45.7 ReasoNet-S  78.2 39.4 50.3 Reinforced Mnemonic Reader-S (Hu et al., 2017) 78.5 46.6 56.0 QANet-S (Yu et al., 2018) 83.8 45.2 55.7 GQA-S (Lewis and Fan, 2018) 83.7 47.3 57.8 FusionNet-E (Huang et al., 2017) 83.6 51.4 60.7 BERT-S (Devlin et al., 2018) 88.5 51.0 63.4 BERT-S + QAInfomax 88.6 54.5 † 64.9 † the experiments, we use S(r a ) = ( 1 M P r a i ) as our summarization function, where is the logistic sigmoid nonlinearity.
a positive sample here is the pair of a answer summary vector s = S(r a ) and all other word representations in r. Negative samples are provided by sampling question, passage and answer representations {r q ,r p ,r a } from an alternative training example.