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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

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Showing 351–400 of 664 papers

TitleStatusHype
QGASP: a Framework for Question Generation Based on Different Levels of Linguistic Information—0
QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation—0
QOG:Question and Options Generation based on Language Model—0
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance—0
QSTS: A Question-Sensitive Text Similarity Measure for Question Generation—0
Question and Answer pair generation for Telugu short stories—0
Question Answering and Question Generation for Finnish—0
Question Answering and Question Generation as Dual Tasks—0
Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices—0
Question Generation and Answering for exploring Digital Humanities collections—0
Question Generation Based on Grammar Knowledge and Fine-grained Classification—0
Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding—0
Question Generation for Generating Textbook Flashcards—0
Question Generation for Language Learning: From ensuring texts are read to supporting learning—0
Question Generation for Question Answering—0
Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask—0
Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask—0
Summary-Oriented Question Generation for Informational Queries—0
Question Generation from a Knowledge Base with Web Exploration—0
Question Generation from Paragraphs: A Tale of Two Hierarchical Models—0
Question Generation from SQL Queries Improves Neural Semantic Parsing—0
Question Generation in Knowledge-Driven Dialog: Explainability and Evaluation—0
Question Generation using a Scratchpad Encoder—0
Question-type Driven Question Generation—0
QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis—0
QuOTE: Question-Oriented Text Embeddings—0
QURIOUS: Question Generation Pretraining for Text Generation—0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models—0
Ranking Model with a Reduced Feature Set for an Automated Question Generation System—0
Reasoning Circuits: Few-shot Multihop Question Generation with Structured Rationales—0
Recent Advances in Neural Question Generation—0
Reference-based Metrics Disprove Themselves in Question Generation—0
Reframing Instructional Prompts to GPTk's Language—0
Reinforced Multi-task Approach for Multi-hop Question Generation—0
Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions—0
Restatement and Question Generation for Counsellor Chatbot—0
Rethinking the Agreement in Human Evaluation Tasks—0
Retrieval-guided Counterfactual Generation for QA—0
Retrieval-guided Counterfactual Generation for QA—0
Review-based Question Generation with Adaptive Instance Transfer and Augmentation—0
RevUP: Automatic Gap-Fill Question Generation from Educational Texts—0
RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library—0
s2s-ft: Fine-Tuning Pretrained Transformer Encoders for Sequence-to-Sequence Learning—0
Savaal: Scalable Concept-Driven Question Generation to Enhance Human Learning—0
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation—0
Selecting Domain-Specific Concepts for Question Generation With Lightly-Supervised Methods—0
Self-Attention Architectures for Answer-Agnostic Neural Question Generation—0
Self Rewarding Self Improving—0
Self-supervised clarification question generation for ambiguous multi-turn conversation—0
Self-Training for Jointly Learning to Ask and Answer Questions—0
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All datasetsSQuAD1.1COCO Visual Question Answering (VQA) real images 1.0 open endedGrailQA-CompositionalGrailQA-IIDGrailQA-Zero-ShotFairytaleQAWeiboPollsNatural QuestionsSQuADTriviaQAVisual Question Generation

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