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Question Generation On Squad11

Metrics

BLEU-4
METEOR
ROUGE-L

Results

Performance results of various models on this benchmark

Model Name
BLEU-4
METEOR
ROUGE-L
Paper TitleRepository
ProphetNet + syn. mask + localness24.3726.2652.77Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation-
UniLMv224.43--UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training-
ERNIE-GENLARGE (beam size=5)25.41--ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation-
ProphetNet23.9126.652.3ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training-
UniLM + ASGen23.725.952.3Learning to Generate Questions by Recovering Answer-containing Sentences-
MPQG13.91--Leveraging Context Information for Natural Question Generation
ProphetNet + ASGen24.4426.7352.8Learning to Generate Questions by Recovering Answer-containing Sentences-
RNN +attn +copy13.5--Evaluating Rewards for Question Generation Models-
UniLM22.7825.151.1Unified Language Model Pre-training for Natural Language Understanding and Generation-
NQG++13.27--Neural Question Generation from Text: A Preliminary Study-
BART (TextBox 2.0)25.0826.7352.55TextBox 2.0: A Text Generation Library with Pre-trained Language Models-
BERTSQG22.17--A Recurrent BERT-based Model for Question Generation
Selector & NQG++15.874--Mixture Content Selection for Diverse Sequence Generation-
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