S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
Table 3: The performance on the MS-MARCO development set in terms of ROUGE-L. *Using the ensemble result of extraction models as the input of the synthesis model. +Wang & Jiang (2016b) report their Prediction with 37.3.
['[BOLD] Method', '[BOLD] Extraction', '[BOLD] Extraction  [BOLD] +Synthesis']
Since answers on the test set are not published, we analyze our model on the development set. For the evidence extraction part, our proposed multi-task learning framework achieves 42.23 and 44.11 for the single and ensemble model in terms of ROUGE-L. For the answer synthesis, the single and ensemble models improve 3.72% and 3.65% respectively in terms of ROUGE-L. We observe the consistent improvement when applying our answer synthesis model to other answer span prediction models, such as BiDAF and Prediction.