Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
Table 6: Performance of baseline in accuracy (%) on the C{}^{3} dataset (\ast: based on the annotated subset of test and development sets of C{}^{3}).
['[BOLD] Method', '{\\text{C}}_{\\text{M}}^{3}  [BOLD] Dev', '{\\text{C}}_{\\text{M}}^{3}  [BOLD] Test', '{\\text{C}}_{\\text{D}}^{3}  [BOLD] Dev', '{\\text{C}}_{\\text{D}}^{3}  [BOLD] Test', '[BOLD] C{}^{3}  [BOLD] Dev', '[BOLD] C{}^{3}  [BOLD] Test']
BERT-wwm-ext performs better on C{}^{3} compared to other three pre-trained language models, though there still exists a large gap (27.5\%) between this method and human performance (96.0\%). Besides augmenting the training data with translated instances, we also attempt to fine-tune a pre-trained multilingual BERT-Base released by \newcitebert2018 on the training data of C{}^{3} and all original training instances in English from RACE and DREAM.