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 601650 of 664 papers

TitleStatusHype
Neural Models for Key Phrase Extraction and Question Generation0
Automatic Question Generation using Relative Pronouns and Adverbs0
Learning to Automatically Generate Fill-In-The-Blank Quizzes0
Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction0
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations0
A Semantic Role-based Approach to Open-Domain Automatic Question Generation0
Leveraging Context Information for Natural Question GenerationCode0
Self-Training for Jointly Learning to Ask and Answer Questions0
Learning to Collaborate for Question Answering and Asking0
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation0
Harvesting Paragraph-Level Question-Answer Pairs from WikipediaCode0
Learning to Ask Questions in Open-domain Conversational Systems with Typed DecodersCode0
Customized Image Narrative Generation via Interactive Visual Question Generation and Answering0
Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering0
Automating Reading Comprehension by Generating Question and Answer Pairs0
Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity TypesCode0
Topic-Based Question Generation0
A Syntactic Approach to Domain-Specific Automatic Question Generation0
Plan, Attend, Generate: Planning for Sequence-to-Sequence ModelsCode1
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards0
iVQA: Inverse Visual Question Answering0
Visual Question Generation as Dual Task of Visual Question Answering0
A Unified Query-based Generative Model for Question Generation and Question Answering0
Question Generation for Language Learning: From ensuring texts are read to supporting learning0
Multiple Choice Question Generation Utilizing An Ontology0
Question Generation for Question Answering0
Identifying Where to Focus in Reading Comprehension for Neural Question Generation0
Learning to Disambiguate by Asking Discriminative Questions0
Crowdsourcing Multiple Choice Science Questions0
Domain Specific Automatic Question Generation from Text0
Neural Models for Key Phrase Detection and Question Generation0
Question Answering and Question Generation as Dual Tasks0
A Joint Model for Question Answering and Question Generation0
Machine Comprehension by Text-to-Text Neural Question GenerationCode0
The Forgettable-Watcher Model for Video Question Answering0
Learning to Ask: Neural Question Generation for Reading ComprehensionCode1
Creativity: Generating Diverse Questions using Variational Autoencoders0
Neural Question Generation from Text: A Preliminary StudyCode1
Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model0
Automatic Generation of Grounded Visual Questions0
Probabilistic Prototype Model for Serendipitous Property Mining0
Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts0
Question Generation from a Knowledge Base with Web Exploration0
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Good Automatic Authentication Question Generation0
Infusing NLU into Automatic Question Generation0
QGASP: a Framework for Question Generation Based on Different Levels of Linguistic Information0
Selecting Domain-Specific Concepts for Question Generation With Lightly-Supervised Methods0
Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization0
Language Muse: Automated Linguistic Activity Generation for English Language Learners0
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Benchmark Results

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