Leaderboard for Du et al 2017 split of SQuAD

	Model	Author			BLEU	Verified
1	UniLM Unified Language Model Pre-training for Natural Language Understanding and Generation	Dong et al (2019) Microsoft	Paper	Code	22.78
2	QPP&QAP Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering	Zhang and Bansal (2019) UNC Chapel Hill	Paper	Code	18.37
3	G2S +BERT+RL Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation	Chen et al (2019) RPI/IBM	Paper		17.94
4	TTPC Improving Question Generation With to the Point Context	Li et al (2019) CUHK	Paper		16.27
5	ASs2s Improving Neural Question Generation using Answer Separation	Kim et al (2018) SNC	Paper		16.2
6	Seq2Seq +copy +attn +coref Harvesting Paragraph-level Question-Answer Pairs from Wikipedia	Du et al (2018) Cornell	Paper		15.16
7	M2S+cp Leveraging Context Information for Natural Question Generation	Song et al (2018) University of Rochester/IBM	Paper		13.98
8	Seq2Seq +copy +attn A RNN Seq2Seq architecture with attention and copy mechanism	Tom Hosking (2019) UCL	Paper	Code	13.5
9	Seq2Seq Learning to Ask: Neural Question Generation for Reading Comprehension	Du et al (2017) Cornell	Paper		12.28
10	PCFG-Trans Question Generation via Overgenerating Transformations and Ranking	Heilman and Smith (2011) CMU	Paper	Code	9.12
11	Baseline - sentence Take the sentence that the answer span falls within as the question	- (2019) -		Code	4.93

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