Multi-hop Reading Comprehension through Question Decomposition and Rescoring
Table 3: F1 scores on the dev set of HotpotQA in both distractor (left) and full wiki settings (right). We compare DecompRC (our model), BERT, and BiDAF, and variants of the models that are only trained on single-hop QA data (1hop train). Bridge and Comp indicate original splits in HotpotQA; Single and Multi refer to dev set splits that can be solved (or not) by all of three BERT models trained on single-hop QA data.
['[EMPTY]', '[BOLD] Distractor setting All', '[BOLD] Distractor setting Bridge', '[BOLD] Distractor setting Comp', '[BOLD] Distractor setting Single', '[BOLD] Distractor setting Multi', '[BOLD] Full wiki setting All', '[BOLD] Full wiki setting Bridge', '[BOLD] Full wiki setting Comp', '[BOLD] Full wiki setting Single', '[BOLD] Full wiki setting Multi']
We observe that DecompRC outperforms all baselines in both distractor and full wiki settings, outperforming the previous published result by a large margin. An interesting observation is that DecompRC not trained on multi-hop QA pairs (DecompRC–1hop train) shows reasonable performance across all data splits.