Almost all previous state-of-the-art QA and RC models find answers by matching pas- DrQA (Chen et al., 2017) 37.7 44.5 41.9 48.7 32.3 38.3 29.8 -R 3 (Wang et al., 2018a) 35.3 41.7 49.0 55.3 47.3 53.7 29.1 37.5 OpenQA (Lin et al., 2018) 42.2 49.3 58.8 64.5 48.7 56.3 28.7 36.6 TraCRNet (Dehghani et al., 2019) 43.2 54.0 52.9 65.1 ----HAS-QA (Pang et al., 2019) 43.2 48.9 62.7 68.7 63.6 68.9 --BERT (Large) (Nogueira et al.,   sages with questions, aka inter-sentence matching (Wang and Jiang, 2017;Wang et al., 2016;Seo et al., 2017;Song et al., 2017).
bERT model simply concatenates a passage with a question, and differentiates them by separating them with a delimiter token [SEP], and assigning different segment ids for them.