A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction
Table 4: Experimental results on the test set of Quasar-T. R3 is a RL-based method. Results of GA, BiDAF and R3 are copied from (Lin et al., 2018). DSQA+STM outperforms the best baseline (DSQA+DS) significantly (t-test, p-value <0.05, DS=distant supervision).
['Model', 'EM', 'F1']
Compared with the base models DSQA and DSQA+DS, DSQA+STM achieved better performance in both metrics, which verifies that DSQA can also benefit from Self-Training. Our method is general and can improve both lightweight and heavyweight models, like LSTM-based and BERT-based models, in different tasks.