A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction
Table 5: Evidence extraction evaluation on the development sets of CoQA and MultiRC. P@k / R@k represent precision / recall of the generated evidence labels, respectively for top k predicted evidence sentences.
['Model/Dataset', 'CoQA P@1', 'MultiRC R@1', 'MultiRC R@2', 'MultiRC R@3', 'MultiRC P@1', 'MultiRC P@2', 'MultiRC P@3']
It can be seen that BERT-HA+STM outperformed the base model BERT-HA by a large margin in terms of all the metrics. As a result, the evidence extractor augmented with STM provided more evidential information for the answer predictor, which may explain the improvements of BERT-HA+STM on the two datasets.