Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Table 3: Performance of our method and competing models on the MS-MARCO test set
['Model', 'ROUGE-L', 'BLEU-1']
et al. As we can see, for both metrics, our single model outperforms all the other competing models with an evident margin, which is extremely hard considering the near-human performance. If we ensemble the models trained with different random seeds and hyper-parameters, the results can be further improved and outperform the ensemble model in \newcitesnet, especially in terms of the BLEU-1.