SOTAVerified

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

Showing 51–100 of 664 papers

TitleStatusHype
A Feasibility Study of Answer-Agnostic Question Generation for EducationCode1
IT5: Text-to-text Pretraining for Italian Language Understanding and GenerationCode1
QA4QG: Using Question Answering to Constrain Multi-Hop Question GenerationCode1
Leaf: Multiple-Choice Question GenerationCode1
QAFactEval: Improved QA-Based Factual Consistency Evaluation for SummarizationCode1
Automated question generation and question answering from Turkish textsCode1
MixQG: Neural Question Generation with Mixed Answer TypesCode1
Context-NER : Contextual Phrase Generation at ScaleCode1
TruthfulQA: Measuring How Models Mimic Human FalsehoodsCode1
Transformer Models for Text Coherence AssessmentCode1
Contrastive Domain Adaptation for Question Answering using Limited Text CorporaCode1
Semantic-Based Self-Critical Training For Question GenerationCode1
Continuous Language Generative FlowCode1
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic RewardsCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge GraphsCode1
Question Generation for Adaptive EducationCode1
ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language GenerationCode1
EL-Attention: Memory Efficient Lossless Attention for GenerationCode1
Diverse and Specific Clarification Question Generation with KeywordsCode1
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage RetrievalCode1
Q^2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question AnsweringCode1
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic EvaluationCode1
Ask what's missing and what's useful: Improving Clarification Question Generation using Global KnowledgeCode1
Automatically Generating Cause-and-Effect Questions from PassagesCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph DocumentsCode1
Quiz-Style Question Generation for News StoriesCode1
ChainCQG: Flow-Aware Conversational Question GenerationCode1
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale PretrainingCode1
Contrastive Learning with Adversarial Perturbations for Conditional Text GenerationCode1
EQG-RACE: Examination-Type Question GenerationCode1
Just Ask: Learning to Answer Questions from Millions of Narrated VideosCode1
Exploring Question-Specific Rewards for Generating Deep QuestionsCode1
PathQG: Neural Question Generation from FactsCode1
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question AnsweringCode1
Unsupervised Multi-hop Question Answering by Question GenerationCode1
Multi-hop Question Generation with Graph Convolutional NetworkCode1
Contrast and Classify: Training Robust VQA ModelsCode1
Mathematical Word Problem Generation from Commonsense Knowledge Graph and EquationsCode1
Evaluating Factuality in Generation with Dependency-level EntailmentCode1
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine ReadingCode1
Inquisitive Question Generation for High Level Text ComprehensionCode1
Sequence-to-Sequence Learning for Indonesian Automatic Question GeneratorCode1
Can questions summarize a corpus? Using question generation for characterizing COVID-19 researchCode1
Text Generation by Learning from DemonstrationsCode1
A Dataset and Baselines for Visual Question Answering on ArtCode1
Investigating Pretrained Language Models for Graph-to-Text GenerationCode1
Visual Question Generation from Radiology ImagesCode1
ClarQ: A large-scale and diverse dataset for Clarification Question GenerationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ERNIE-GENLARGE (beam size=5)BLEU-425.41—Unverified
2BART (TextBox 2.0)BLEU-425.08—Unverified
3ProphetNet + ASGenBLEU-424.44—Unverified
4UniLMv2BLEU-424.43—Unverified
5ProphetNet + syn. mask + localnessBLEU-424.37—Unverified
6ProphetNetBLEU-423.91—Unverified
7UniLM + ASGenBLEU-423.7—Unverified
8UniLMBLEU-422.78—Unverified
9BERTSQGBLEU-422.17—Unverified
10Selector & NQG++BLEU-415.87—Unverified
#ModelMetricClaimedVerifiedStatus
1MDNBLEU-165.1—Unverified
2coco-Caption [[Karpathy and Li2014]]BLEU-162.5—Unverified
3Max(Yang,2015)BLEU-159.4—Unverified
4Sample(Yang,2015)BLEU-138.8—Unverified
#ModelMetricClaimedVerifiedStatus
1FactJointGTMETEOR36.21—Unverified
2JointGTMETEOR36.08—Unverified
3FactT5BMETEOR35.72—Unverified
4T5BMETEOR35.64—Unverified
#ModelMetricClaimedVerifiedStatus
1FactT5BBLEU46.1—Unverified
2JointGTBLEU45.95—Unverified
3T5BBLEU44.51—Unverified
4FactJointGTBLEU43.61—Unverified
#ModelMetricClaimedVerifiedStatus
1JointGTMETEOR37.69—Unverified
2FactJointGTMETEOR37.55—Unverified
3FactT5BMETEOR37.39—Unverified
4T5BMETEOR37.35—Unverified
#ModelMetricClaimedVerifiedStatus
1BART fine-tuned on FairytaleQAROUGE-L0.53—Unverified
2BART fine-tuned on NarrativeQA and FairytaleQAROUGE-L0.52—Unverified
3BART fine-tuned on NarrativeQAROUGE-L0.44—Unverified
#ModelMetricClaimedVerifiedStatus
1UniPollROUGE-149.6—Unverified
2T5ROUGE-144.46—Unverified
3Dual DecROUGE-138.24—Unverified
#ModelMetricClaimedVerifiedStatus
1Info-HCVAEQAE37.18—Unverified
2HCVAEQAE31.45—Unverified
#ModelMetricClaimedVerifiedStatus
1Info-HCVAEQAE71.18—Unverified
2HCVAEQAE69.46—Unverified
#ModelMetricClaimedVerifiedStatus
1Info-HCVAEQAE35.45—Unverified
2HCVAEQAE30.2—Unverified
#ModelMetricClaimedVerifiedStatus
1MDNBLEU-136—Unverified