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 351375 of 664 papers

TitleStatusHype
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation0
Synthetic Context Generation for Question Generation0
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering0
Synthetic Multimodal Question Generation0
Temporal Question Generation from History Text0
Textual Entailment based Question Generation0
The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation0
The Forgettable-Watcher Model for Video Question Answering0
The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models0
The Role of Large Language Models in Musicology: Are We Ready to Trust the Machines?0
TiBERT: Tibetan Pre-trained Language Model0
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs0
TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference0
Topic-Based Question Generation0
Towards Answer-unaware Conversational Question Generation0
Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues0
Towards automatically generating Questions under Discussion to link information and discourse structure0
Towards Automatic Generation of Questions from Long Answers0
Towards Automatic Topical Question Generation0
Towards Topic-to-Question Generation0
TransientTables: Evaluating LLMs' Reasoning on Temporally Evolving Semi-structured Tables0
Translation of Multifaceted Data without Re-Training of Machine Translation Systems0
TRUE: Re-evaluating Factual Consistency Evaluation0
Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering0
Type-dependent Prompt CycleQAG : Cycle Consistency for Multi-hop Question 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