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 501525 of 664 papers

TitleStatusHype
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases0
Meta-X_NLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation0
Mind the Gap: Learning to Choose Gaps for Question Generation0
MIRROR: A Novel Approach for the Automated Evaluation of Open-Ended Question Generation0
MixQG: Neural Question Generation with Mixed Answer Types0
MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants0
MTG: A Benchmarking Suite for Multilingual Text Generation0
Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity0
Multi-hop Evidence Pursuit Meets the Web: Team Papelo at FEVER 20240
Multilingual Open QA on the MIA Shared Task0
Multimodal Differential Network for Visual Question Generation0
Multiple-Choice Question Generation: Towards an Automated Assessment Framework0
Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights0
Multiple Choice Question Generation Utilizing An Ontology0
Multi-Task Learning with Language Modeling for Question Generation0
Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features0
Neural Models for Key Phrase Detection and Question Generation0
Neural Models for Key Phrase Extraction and Question Generation0
Neural Question Generation using Interrogative Phrases0
Neural Self Talk: Image Understanding via Continuous Questioning and Answering0
NewsQs: Multi-Source Question Generation for the Inquiring Mind0
OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach0
Online shopping behavior study based on multi-granularity opinion mining: China vs. America0
On the Importance of Diversity in Question Generation for QA0
On Training Instance Selection for Few-Shot Neural Text Generation0
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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