natural-language-processing-19
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-19
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-19 models and implementations
4 papers 17,208
Datasets
No datasets available.
