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 251–260 of 664 papers

Title	Date	Tasks	Status
AI empowering research: 10 ways how science can benefit from AI	Jul 17, 2023	Question GenerationQuestion-Generation	—Unverified
Deep Learning on Graphs for Natural Language Processing	Jun 1, 2021	Deep Learninggraph construction	—Unverified
Generating Natural Questions from Images for Multimodal Assistants	Nov 17, 2020	Common Sense ReasoningNatural Questions	—Unverified
Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts	Dec 1, 2016	coreference-resolutionCoreference Resolution	—Unverified
Deep Exemplar Networks for VQA and VQG	Dec 19, 2019	DecoderQuestion Answering	—Unverified
Automatic Learning Assistant in Telugu	Aug 1, 2021	Hybrid Machine LearningPOS	—Unverified
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models	Sep 21, 2024	Contrastive LearningHallucination	—Unverified
Deep Bayesian Network for Visual Question Generation	Jan 23, 2020	Natural QuestionsQuestion Generation	—Unverified
Decision-Theoretic Question Generation for Situated Reference Resolution: An Empirical Study and Computational Model	Oct 12, 2021	Question GenerationQuestion-Generation	—Unverified
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation	Oct 27, 2023	Image GenerationQuestion Answering	—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