Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
Table 5: Results on TriviaQA-unfiltered test set and SQuAD-open dev set: Chen et al. (2017)1, Wang et al. (2018a)2, Lin et al. (2018)3, Wang et al. (2018b)4, Min et al. (2018)5, Das et al. (2019)6, Clark and Gardner (2018)7, Pang et al. (2019)8 and Yang et al. (2019)9.
['Model', 'TriviaQA-unfiltered EM', 'TriviaQA-unfiltered F1', 'SQuAD-open EM', 'SQuAD-open F1']
Again, RE3QA surpasses prior works by an evident margin: our best model achieves 71.2 F1 on TriviaQA-unfiltered, and outperforms a BERT baseline by 4 F1 on SQuAD-open, indicating that our approach is effective for the challenging multi-document RC task.