S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
Table 6: The performance on MS-MARCO development set of end-to-end methods.
['[BOLD] Method', '[BOLD] ROUGE-L']
The authors of MS-MACRO publish a baseline of training a sequence-to-sequence model with the question and answer, which only achieves 8.9 in terms of ROUGE-L. Adding all passages to the sequence-to-sequence model can obviously improve the result to 28.75. Then we only use the question and the selected passage to generate the answer. The only difference with our synthesis model is that we add the position features to the basic sequence-to-sequence model. The result is still worse than our synthesis model with a large margin, which shows the matching between question and passage is very important for generating answer. Next, we build an end-to-end framework combining matching and generation. Above results show the effectiveness of our model that solves this task with two steps. In the future, we hope the reinforcement learning can help the connection between evidence extraction and answer synthesis.