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
Automatic Follow-up Question Generation for Asynchronous Interviews0
Evaluating for Diversity in Question Generation over Text0
Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions0
On the Importance of Diversity in Question Generation for QA0
How to Ask Good Questions? Try to Leverage Paraphrases0
Unsupervised FAQ Retrieval with Question Generation and BERT0
Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction0
CopyBERT: A Unified Approach to Question Generation with Self-Attention0
A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems0
ColdGANs: Taming Language GANs with Cautious Sampling Strategies0
A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation0
A Consolidated Dataset for Knowledge-based Question Generation using Predicate Mapping of Linked Data0
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation0
QURIOUS: Question Generation Pretraining for Text Generation0
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation0
Towards Automatic Generation of Questions from Long Answers0
Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing0
Reinforced Multi-task Approach for Multi-hop Question Generation0
Generating Followup Questions for Interpretable Multi-hop Question Answering0
What BERT Sees: Cross-Modal Transfer for Visual Question Generation0
Deep Bayesian Network for Visual Question Generation0
Deep Exemplar Networks for VQA and VQG0
Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation0
Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring0
A Free Lunch in Generating Datasets: Building a VQG and VQA System with Attention and Humans in the Loop0
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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