DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension
Table 6: Results on the test set of SemEval Task 11, ROCStories, MCTest and the development set of COIN Task 1. The test set of COIN is not public. DCMN_BERT: BERT + DCMN + PSS + AOI. Previous SOTA: previous state-of-the-art model. All the results are from single models.
['Task', 'Previous STOA', '[EMPTY]', 'BERT', 'DCMN_BERT', 'XLNet', 'DCMN_XLNet']
Passage sentence selection is not used in this dataset because there are only four sentences as the passage. Since the test set of COIN is not publicly available, we report the performance of the model on its development set. To further investigate the contribution of our model, we also report the results of directly fine-tuned BERT/ XLNet on the target datasets. From the comparison, we can see that our model obtains 4.9% and 2.8% absolute improvement in average accuracy over the baseline of directly fine-tuned BERT (88.9% vs. 84.0%) and XLNet (90.6% vs. 87.8%), respectively. These results indicate our proposed model has a heavy impact on the performance no matter how strong the adopted pre-trained language model itself is.