Generating Natural Language Questions to Support Learning On-Line

By David Lindberg, Fred Popowich, John C. Nesbit, Philip H. Winne. 2013

157 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (2019, background): “Question Generation Early QG studies focused on using rule-based methods to transform statements to questions (Heilman and Smith, 2010; Lindberg et al., 2013; Labutov et al., 2015).”
Learning to Ask: Neural Question Generation for Reading Comprehension (2017, background): “A lot of research has focused on ﬁrst manually constructing question templates, and then applying them to generate questions (Mostow and Chen, 2009; Lindberg et al., 2013; Mazidi and Nielsen, 2014).”
G-DAug: Generative Data Augmentation for Commonsense Reasoning (2020, neutral): “, 2011), online tutoring (Lindberg et al., 2013), factual QA”
Machine Comprehension by Text-to-Text Neural Question Generation (2017, background): “Several earlier works process documents as individual sentences using syntactic (Heilman and Smith, 2010; Ali et al., 2010; Kumar et al., 2015) or semantic-based parsing (Mannem et al., 2010; Lindberg et al., 2013), then reformulate questions using hand-crafted rules acting on parse trees.”
Low-Resource Generation of Multi-hop Reasoning Questions (2020, methodology): “QG is widely used in many practical scenarios: including providing practice exercises from course materials for educational purposes (Lindberg et al., 2013), initiating the dialog system by asking questions (Mostafazadeh et al., 2017), and reducing the labor cost of creating large-scale labeled…”
Inducing Self-Explanation: a Meta-Analysis (2018, unknown): “Two of the authors have collaborated with computer scientists in developing and evaluating algorithms that automatically generate questions for expository texts (Lindberg et al. 2013; Odilinye et al. 2015).”
Neural Models for Key Phrase Extraction and Question Generation (2018, methodology): “Various NLP techniques have been adopted in these systems to improve generation quality, including parsing (Heilman and Smith, 2010a; Mitkov and Ha, 2003), semantic role labeling (Lindberg et al., 2013), and the use of lexicographic resources like WordNet (Miller, 1995; Mitkov and Ha, 2003).”
Deep Questions without Deep Understanding (2015, background): “Much work to date has focused on questions based on a single sentence of the text (Becker et al., 2012; Lindberg et al., 2013; Mazidi and Niel-sen, 2014), and the ideal of creating deep, conceptual questions has remained elusive.”
Generating Natural Questions About an Image (2016, methodology): “For the latter, several methods have been explored: (Becker et al., 2012) create ﬁll-in-the-blank questions, (Mazidi and Nielsen, 2014) and (Lindberg et al., 2013) use manually constructed question templates, while (Labutov et al., 2015) use crowd-sourcing to collect a set of templates and then…”