natural-language-processing
Question Generation
3260 papers • 126 benchmarks • 313 datasets
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
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in question-generation
Trend
Dataset
Best Model
Actions
SQuAD1.1
SQuAD1.1
COCO Visual Question Answering (VQA) real images 1.0 open ended
COCO Visual Question Answering (VQA) real images 1.0 open ended
Libraries
iUse these libraries to find question-generation models and implementations
4 papers 17,208
Datasets
Subtasks
Most implemented papers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Noam Shazeer, Peter J. Liu, Sharan Narang, Yanqi Zhou, Adam Roberts, Colin Raffel, Katherine Lee, Michael Matena, Wei Li•Tue Oct 22 2019
This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.
