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 301325 of 664 papers

TitleStatusHype
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages0
On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question GenerationCode0
IT5: Text-to-text Pretraining for Italian Language Understanding and GenerationCode1
QA4QG: Using Question Answering to Constrain Multi-Hop Question GenerationCode1
Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding0
Unified Question Generation with Continual Lifelong Learning0
Leaf: Multiple-Choice Question GenerationCode1
Improving Biomedical Information Retrieval with Neural Retrievers0
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Graph0
JEFF - Just Another EFFicient Reading Comprehension Test Generation0
Consecutive Question Generation with Multitask Joint Reranking and Dynamic Rationale Search0
All You May Need for VQA are Image Captions0
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization0
TRUE: Re-evaluating Factual Consistency Evaluation0
Data Augmentation for Biomedical Factoid Question Answering0
MixQG: Neural Question Generation with Mixed Answer Types0
Cooperative Self-training of Machine Reading Comprehension0
V-Doc: Visual Questions Answers With Documents0
An ASP-based Approach to Answering Natural Language Questions for Texts0
Self-supervised clarification question generation for ambiguous multi-turn conversation0
QAFactEval: Improved QA-Based Factual Consistency Evaluation for SummarizationCode1
ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs0
Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices0
Improving Controllability of Educational Question Generation by Keyword Provision0
Temporal Question Generation from History Text0
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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