Automatic Question Generation for Vocabulary Assessment

By Jonathan Brown, G. Frishkoff, Maxine Eskénazi. Human Language Technology - The Baltic Perspectiv, 2005

303 citations recorded by the source corpus.

In the REAP system, users are automatically provided with texts to read targeted to their individual reading levels. To find appropriate texts, the user's vocabulary knowledge must be assessed. We describe an approach to automatically generating questions for vocabulary assessment. Traditionally, these assessments have been hand-written. Using data from WordNet, we generate 6 types of vocabulary questions. They can have several forms, including wordbank and multiple-choice. We present experimental results that suggest that these automatically-generated questions give a measure of vocabulary skill that correlates well with subject performance on independently developed human-written questions. In addition, strong correlations with standardized vocabulary tests point to the validity of our approach to automatic assessment of word knowledge.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/2f9e088b58b1fa5da2539a9e4158570039473a69

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In Artificial Intelligence

Citations in the corpus

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, background): “…of peer reviews for scientific papers (Bartoli et al., 2016); • generation of paraphrases of input sentences (e.g., Bannard & CallisonBurch, 2005; Kauchak & Barzilay, 2006); and • automatic generation of questions, for educational and other purposes (e.g., Brown et al., 2005; Rus et al., 2010).”
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards (2017, background): “Question Generation in NLP There is a long history of works on grammar question generation from text domain in natural language processing (NLP) [6, 9, 25, 29].”
Automatic Gap-fill Question Generation from Text Books (2011, background): “For distractor selection , Brown et al. (2005) The fatty acid will have a kink in its tail wherever a double bond occurs.”
Multiple Choice Question Generation Utilizing An Ontology (2017, background): “Approaches have included factual generation directly from text (Brown et al., 2005; Mannem et al., 2010; Mazidi and Tarau, 2016; Yao et al., 2012) as well as generation from knowledge bases (Olney et al., 2012a).”
A Data Repository for the EDM Community: The PSLC DataShop (2010, background): “edu) [13], Optimized vocabulary practice [28], and Assistments (assistments.”
Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model (2017, background): “A number of papers have looked at the problem of generating vocabulary questions using Word-Net (Miller et al., 1990) and distributional similarity techniques (Brown et al., 2005; Heilman and Eskenazi, 2007).”