Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering
Table 5: The results of our semi-supervised QA method using a BiDAF-QA model.
['[EMPTY]', 'Data', 'Data Size', 'EM', 'F1']
Without introducing new articles, we keep beam search outputs as additional questions. It can be seen that using beam search with beam size 10 (+Beam10) improves the BiDAF-QA baseline by 1.51/1.13 absolute points on the testing set. With introducing new articles, the best performance (+H8) improves the BiDAF-QA baseline by 1.69/1.27 absolute points on the testing set. We also combine the two best settings (Beam10+H8), but it does not perform better than using them separately.