Concepts de base
The author explores the advancements in Neural Question Generation (NQG) by categorizing approaches and discussing their strengths and limitations.
Résumé
This survey delves into the background, methods, applications, and future trends of NQG. It covers structured NQG using knowledge bases, unstructured NQG from texts and images, and hybrid approaches. The paper highlights the shift from rule-based to neural network-based models like PLMs for improved performance in question generation tasks.
Stats
"22,989 instances from WebQuestionsSP and ComplexWebQuestions" - Kumar et al., 2019a.
"3,563,535 questions in MS MARCO dataset" - Nguyen et al., 2016.
"369,861 questions in VQA dataset" - Antol et al., 2015.
"127,000 QA pairs in CoQA dataset" - Reddy et al., 2019.
"25,000 questions in VQG Commonsense dataset" - Mostafazadeh et al., 2016.
Citations
"In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG)." - Authors
"The field has progressively transitioned from rule-based approaches to neural network-based methods." - Authors
"With the continuous scaling of PLMs in terms of parameter size and training corpus volume..." - Authors