Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
Table 3: Human evaluation results on the SQuAD split-2 test set.
['Methods', 'Syntactically correct (%)', 'Semantically correct (%)', 'Relevant (%)']
We can see that our best performing model achieves good results even compared to the ground-truth, and outperforms the strong baseline method MPQG+R. Our error analysis shows that main syntactic error occurs in repeated/unknown words in generated questions. Further, the slightly lower quality on semantics also impacts the relevance.