QGASP: a Framework for Question Generation Based on Different Levels
            of Linguistic Information

Rodrigues, Hugo; Coheur, Luísa; Nyberg, Eric

doi:10.18653/v1/w16-6640

Cited by 5 publications

(1 citation statement)

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“…They employed the Lexical-Functional Grammar representation, which includes both syntactic and semantic layers. Furthermore, many AQG systems, as per research [5,6], rely on semantic role labeling as the primary driver of linguistic analysis, or as a supporting subsystem [4].…”

Section: Introductionmentioning

confidence: 99%

Rule-Based Automatic Question Generationusing Dependency Parsing

Sewunetie

Kovács

2023

Preprint

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Artificial intelligence systems offer valuable support to the education sector by automating routine tasks for instructors and providingadaptive assessments. These assessments are a crucial component ofautomatic question-generation techniques. There are various techniquesfor question generation described in the literature. This paper introduces a novel rule-based system for automatic question generationthat employs dependency parsing techniques. The proposed methodfocuses on analyzing both the syntactic and semantic structure of asentence. The paper includes both manual and automatic evaluationmetrics to assess the questions generated by our proposed system. Inthe evaluation, our system received an overall score of 3.67 out of5.0 for human-level performance, an average BlEU−N score of 0.718,and F1−score scores above 0.5 for all types of ROUGE. Both humanand automatic evaluation results demonstrate that our proposed system achieves good performance on simple and short sentences. In thefuture, we plan to enhance our model by incorporating phrase-levelparsing to further improve the proposed dependency parsing techniques.

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Section: Introductionmentioning

confidence: 99%