Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering
Table 6: Comparison of AIR with XLNet, RoBERTa, and BERT on justification selection task, trained on increasing proportion of the training data on MultiRC.
['%\xa0of training data', 'BERT', 'XLnet', 'RoBERTa', '[BOLD] AIR']
This analysis indicates that all three classifiers approach their best performance at around 5% of the training data. This indicates that, while these supervised methods converge quickly, they are unlikely to outperform AIR, an unsupervised method, even if more training data were available.