Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels
Alireza Naeiji, Aijun An, Heidar Davoudi, Marjan Delpisheh, Muath Alzghool · 2023
Automatic generation of questions from text has gained increasing attention due to its useful applications.We propose a novel question generation method that combines the benefits of rule-based and neural sequence-to-sequence (Seq2Seq) models.The proposed method can automatically generate multiple questions from an input sentence covering different views of the sentence as in rule-based methods, while more complicated "rules" can be learned via the Seq2Seq model.The method utilizes semantic role labeling to convert training examples into their semantic representations, and then trains a Seq2Seq model over the semantic representations.Our extensive experiments on three realworld data sets show that the proposed method significantly improves the state-of-the-art neural question generation approaches.