Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
Table 1: Automatic evaluation results on the SQuAD test set.
['Methods', 'BLEU-4', 'Split-1 METEOR', 'ROUGE-L', 'Q-BLEU1', 'BLEU-4', 'Split-2 METEOR', 'ROUGE-L', 'Q-BLEU1']
First of all, we can see that both of our full models G2Ssta+BERT+RL and G2Sdyn+BERT+RL achieve the new state-of-the-art scores on both data splits and consistently outperform previous methods by a significant margin. This highlights that our RL-based Graph2Seq model, together with the deep alignment network, successfully addresses the three issues we highlighted in Sec. Between these two variants, G2Ssta+BERT+RL outperforms G2Sdyn+BERT+RL on all the metrics. Also, unlike the baseline methods, our model does not rely on any hand-crafted rules or ad-hoc strategies, and is fully end-to-end trainable.