Question Generation

The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation.

Source: Generating Highly Relevant Questions

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 601–610 of 664 papers

Title	Date	Tasks	Status
Neural Models for Key Phrase Extraction and Question Generation	Jul 1, 2018	Question AnsweringQuestion Generation	—Unverified
Automatic Question Generation using Relative Pronouns and Adverbs	Jul 1, 2018	DescriptiveDialogue Generation	—Unverified
Learning to Automatically Generate Fill-In-The-Blank Quizzes	Jun 12, 2018	BIG-bench Machine LearningDeep Learning	—Unverified
Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction	Jun 11, 2018	Question GenerationQuestion-Generation	—Unverified
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations	Jun 8, 2018	DiversityMulti-Label Classification	—Unverified
A Semantic Role-based Approach to Open-Domain Automatic Question Generation	Jun 1, 2018	Dependency ParsingQuestion Generation	—Unverified
Leveraging Context Information for Natural Question Generation	Jun 1, 2018	PositionQuestion Generation	CodeCode Available
Self-Training for Jointly Learning to Ask and Answer Questions	Jun 1, 2018	Data AugmentationQuestion Answering	—Unverified
Learning to Collaborate for Question Answering and Asking	Jun 1, 2018	Answer SelectionGenerative Adversarial Network	—Unverified
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation	May 28, 2018	Abstractive Text SummarizationDecoder	—Unverified

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All datasets SQuAD1.1 COCO Visual Question Answering (VQA) real images 1.0 open ended GrailQA-Compositional GrailQA-IID GrailQA-Zero-Shot FairytaleQA WeiboPolls Natural Questions SQuAD TriviaQA Visual Question Generation

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ERNIE-GENLARGE (beam size=5)	BLEU-4	25.41	—	Unverified
2	BART (TextBox 2.0)	BLEU-4	25.08	—	Unverified
3	ProphetNet + ASGen	BLEU-4	24.44	—	Unverified
4	UniLMv2	BLEU-4	24.43	—	Unverified
5	ProphetNet + syn. mask + localness	BLEU-4	24.37	—	Unverified
6	ProphetNet	BLEU-4	23.91	—	Unverified
7	UniLM + ASGen	BLEU-4	23.7	—	Unverified
8	UniLM	BLEU-4	22.78	—	Unverified
9	BERTSQG	BLEU-4	22.17	—	Unverified
10	Selector & NQG++	BLEU-4	15.87	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MDN	BLEU-1	65.1	—	Unverified
2	coco-Caption [[Karpathy and Li2014]]	BLEU-1	62.5	—	Unverified
3	Max(Yang,2015)	BLEU-1	59.4	—	Unverified
4	Sample(Yang,2015)	BLEU-1	38.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FactJointGT	METEOR	36.21	—	Unverified
2	JointGT	METEOR	36.08	—	Unverified
3	FactT5B	METEOR	35.72	—	Unverified
4	T5B	METEOR	35.64	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FactT5B	BLEU	46.1	—	Unverified
2	JointGT	BLEU	45.95	—	Unverified
3	T5B	BLEU	44.51	—	Unverified
4	FactJointGT	BLEU	43.61	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	JointGT	METEOR	37.69	—	Unverified
2	FactJointGT	METEOR	37.55	—	Unverified
3	FactT5B	METEOR	37.39	—	Unverified
4	T5B	METEOR	37.35	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BART fine-tuned on FairytaleQA	ROUGE-L	0.53	—	Unverified
2	BART fine-tuned on NarrativeQA and FairytaleQA	ROUGE-L	0.52	—	Unverified
3	BART fine-tuned on NarrativeQA	ROUGE-L	0.44	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniPoll	ROUGE-1	49.6	—	Unverified
2	T5	ROUGE-1	44.46	—	Unverified
3	Dual Dec	ROUGE-1	38.24	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Info-HCVAE	QAE	37.18	—	Unverified
2	HCVAE	QAE	31.45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Info-HCVAE	QAE	71.18	—	Unverified
2	HCVAE	QAE	69.46	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Info-HCVAE	QAE	35.45	—	Unverified
2	HCVAE	QAE	30.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MDN	BLEU-1	36	—	Unverified