S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
Table 4: Results of passage ranking. -w/o Passage Ranking: the model that only has evidence extraction part, without passage ranking part. -Passage Ranking then Extraction: the model that selects the passage firstly and then apply the extraction model only on the selected passage.
['[BOLD] Method', '[BOLD] P@1', '[BOLD] ROUGE-L']
We analyze the result of incorporating passage ranking as an additional task. For passage selection, our multi-task model achieves the accuracy of 38.9, which outperforms the pure answer prediction model with 4.3. Moreover, jointly learning the answer prediction part and the passage ranking part is better than solving this task by two separated steps because the answer span can provide more information with stronger supervision, which benefits the passage ranking part. The ROUGE-L is calculated by the best answer span in the selected passage, which shows our multi-task learning framework has more potential for better answer.