R{}^{3}: Reinforced Ranker-Reader for Open-Domain Question Answering
Table 3: Open-domain question answering results. The results show the average of 5 runs, with standard error in the superscript. The CuratedTREC and WebQuestions models are initialized by training on SQuADOPEN first. On the bottom, YodaQA and DrQA-MTL use additional resources (usage of KB for the former, and multiple training datasets for the latter), so are not a true apple-to-apple comparison to the other methods. EM: Exact Match.
['[EMPTY]', '[BOLD] Quasar-T  [BOLD] F1', '[BOLD] Quasar-T  [BOLD] EM', '[BOLD] SQuADOPEN  [BOLD] F1', '[BOLD] SQuADOPEN  [BOLD] EM', '[BOLD] WikiMovies  [BOLD] F1', '[BOLD] WikiMovies  [BOLD] EM', '[BOLD] CuratedTREC  [BOLD] F1', '[BOLD] CuratedTREC  [BOLD] EM', '[BOLD] WebQuestions  [BOLD] F1', '[BOLD] WebQuestions  [BOLD] EM']
We use F1 score and Exact Match We first observe that on Quasar-T, the Single Reader can exceed state-of-the-art performance.