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Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels

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dc.contributor.advisorAn, Aijun
dc.contributor.authorNaeiji, Alireza
dc.date.accessioned2023-03-28T21:19:44Z
dc.date.available2023-03-28T21:19:44Z
dc.date.copyright2022-12-08
dc.date.issued2023-03-28
dc.date.updated2023-03-28T21:19:44Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractAutomatic 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 (SRL) used in rule-based methods to convert training examples into their semantic representations, and then trains a sequence-to-sequence model over the semantic representations. Our extensive experiments on three real-world data sets show that the proposed method significantly improves the state-of-the-art neural question generation approaches in terms of both automatic and human evaluation measures. Moreover, we extend our proposed approach to a paragraph-level SRL-based method and evaluate it on two data sets. Through both automatic and human evaluations, we show that our proposed framework remarkably improves its Seq2Seq counterparts.
dc.identifier.urihttp://hdl.handle.net/10315/41009
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subjectComputer engineering
dc.subject.keywordsQuestion generation
dc.subject.keywordsSemantic role labeling
dc.subject.keywordsSRL
dc.subject.keywordsSequence-to-sequence
dc.subject.keywordsSeq2Seq
dc.titleQuestion Generation Using Sequence-to-Sequence Model with Semantic Role Labels
dc.typeElectronic Thesis or Dissertation

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