Automatic Gap-fill Question Generation from Text Books

By Manish Agarwal, P. Mannem. 2011

131 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Mohit Bansal
2. Luke S. Zettlemoyer
3. Hannaneh Hajishirzi
4. Jonathan Berant
5. Percy Liang
6. Graham Neubig
7. Matt Gardner
8. Christopher D. Manning

In Evaluation & Benchmarks

1. Luke S. Zettlemoyer
2. Matt Gardner
3. Peter Clark
4. Anton van den Hengel
5. Mohit Bansal
6. Jonathan Berant
7. Ashish Sabharwal
8. Hannaneh Hajishirzi

Citations in the corpus

• MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text (2013, background): “Coming up with incorrect alternatives ( distractors ) is a difficult task (see, e.g., Agarwal, 2011) but workers were requested to provide “reasonable” incorrect answers that at least include words from the story so that their solution is not trivial.”
• Crowdsourcing Multiple Choice Science Questions (2017, methodology): “, 2013) have been used in the past by Agarwal and Mannem (2011) for creating biology questions.”
• Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards (2017, background): “In [1, 4], authors focus on automatically generating gapfill questions, while crowdsourcing templates and manually built templates are used for question generation in [15] and [19] respectively.”
• A Joint Model for Question Answering and Question Generation (2017, unknown): “Earlier work on question generation has resorted to either rule-based reordering methods (Heilman & Smith, 2010; Agarwal & Mannem, 2011; Ali et al., 2010) or slot-filling with question templates (Popowich & Winne, 2013; Chali & Golestanirad, 2016; Labutov et al., 2015).”
• Neural Models for Key Phrase Extraction and Question Generation (2018, methodology): “…to simple, rule-based techniques such as template-based slot fill-ing (Lindberg et al., 2013; Chali and Golestanirad, 2016; Labutov et al., 2015) or syntactic transformation heuristics (Agarwal and Mannem, 2011; Ali et al., 2010) (e.g., subject-auxiliary inversion, (Heilman and Smith, 2010a)).”
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants (2023, background): “Constructing high quality MCQs depends most importantly on creating strong negatives that are neither obviously wrong nor possibly correct (Agarwal and Mannem, 2011; Richardson et al., 2013).”