Question Generation based on Lexico-Syntactic Patterns Learned from the Web

Curto, Sergio; Mendes, Ana Cristina; Coheur, Luísa

doi:10.5087/dad.2012.207

Cited by 30 publications

(25 citation statements)

References 20 publications

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“…The generated questions with those patterns were manually evaluated by two annotators according to a simplification of Curto et al (2012) guidelines: plausible, with exception of minor edits such as verb agreement (y), plausible needing context (c), and implausible (n). There are 201 questions generated, from which 92% are considered plausible of any sort -a total of 184, from which only 32 were labeled as plausible needing context.…”

Section: Discussionmentioning

confidence: 99%

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Proceedings of the 9th International Natural Language Generation conference

Kok

2016

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Section: Discussionmentioning

confidence: 99%

“…As in the TheMentor system (Curto et al, 2012), QGASP (Question Generation with Semantic Patterns) creates patterns based on a set of seeds. However, contrary to TheMentor that relies on lexicon-syntactic patterns, QGASP tries to take advantage of semantic information.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 9th International Natural Language Generation conference

Kok

2016

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“…Major resources are each represented by a single node in the LLOD cloud diagram. These include DBpedia (Mendes et al, 2012), consisting of structured information extracted from Wikipedia; WordNet RDF (McCrae et al, 2014), an RDF translation of Princeton's WordNet lexical database project; and DBnary (Sérasset, 2015), derived from Wiktionary.…”

Section: Llodmentioning

confidence: 99%

“…discusses the generation of factual, low-level questions suitable for beginner or intermediate students and gives a comprehensive overview of QG methods. Among the most prominent ones are: replacing the target form with a gap (Agarwal et al, 2011;Becker et al, 2012), applying transformation rules (Mitkov et al, 2006), filling templates (Curto et al, 2012), and generating all possible questions to a sentence and ranking them afterwards using a supervised learning algorithm . Finally, QG is not an exception to the wave of neural networks, and Du et al (2017) have recently approached automatic generation of reading comprehension questions on that basis.…”

Section: Automatic Question Generationmentioning

confidence: 99%

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Tetreault

Burstein

Leacock³

et al. 2017

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Since the first workshop in 1997, BEA has become the leading venue for sharing and publishing innovative work that uses NLP to develop educational applications. The consistent interest and growth of the workshop has clear ties to challenges in education. The research presented at the workshop highlights advances in the technology and the maturity of the field of NLP in education. The capabilities serve as a response to educational challenges and are poised to support the needs of a variety of stakeholders, including educators, learners, parents, and administrators.NLP capabilities now support an array of learning domains, including writing, speaking, reading, and mathematics. In the writing and speech domains, automated writing evaluation (AWE) and speech assessment applications, respectively, are commercially deployed in high-stakes assessment and instructional settings, including Massive Open Online Courses (MOOCs). We also see widelyused commercial applications for plagiarism detection and peer review and explosive growth of mobile applications for game-based applications for instruction and assessment. The current educational and assessment landscape continues to foster a strong interest and high demand that pushes the state of the art in AWE capabilities to expand the analysis of written responses to writing genres other than those traditionally found in standardized assessments, especially writing tasks requiring use of sources and argumentative discourse.Steady growth in the development of NLP-based applications for education has prompted an increased number of workshops that typically focus on a single subfield. In BEA, we make an effort to have papers from many subfields, for example, tools for automated scoring, automated test-item generation, curriculum development, evaluation of text, dialogue, evaluation of genres beyond essays, feedback studies, and grammatical error correction.This year we received a record 62 submissions, and accepted 9 papers as oral presentations and 25 as poster presentation and/or demos, for an overall acceptance rate of 55 percent. Each paper was reviewed by three members of the Program Committee who were believed to be most appropriate for each paper. We continue to have a very strong policy to deal with conflicts of interest. First, we made a concerted effort to not assign papers to reviewers to evaluate if the paper had an author from their institution. Second, with respect to the organizing committee, authors of papers for which there was a conflict of interest recused themselves from the discussions.While the field is growing, we do recognize that there is a core group of institutions and researchers who work in this area. With a higher acceptance rate, we were able to include papers from a wider variety of topics and institutions. The papers accepted were selected on the basis of several factors, including the relevance to a core educational problem space, the novelty of the approach or domain, and the strength of the research. The accepted papers were highly diverse -...

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“…These approaches can potentially ask deeper questions due to their focus on semantics. A novel question generator by Curto et al (2012) leverages lexicosyntactic patterns gleaned from the web with seed question-answer pairs.…”

Section: Related Workmentioning

confidence: 99%

Linguistic Considerations in Automatic Question Generation

Mazidi¹,

Nielsen²

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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As students read expository text, comprehension is improved by pausing to answer questions that reinforce the material. We describe an automatic question generator that uses semantic pattern recognition to create questions of varying depth and type for self-study or tutoring. Throughout, we explore how linguistic considerations inform system design. In the described system, semantic role labels of source sentences are used in a domain-independent manner to generate both questions and answers related to the source sentence. Evaluation results show a 44% reduction in the error rate relative to the best prior systems, averaging over all metrics, and up to 61% reduction in the error rate on grammaticality judgments.

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Question Generation based on Lexico-Syntactic Patterns Learned from the Web

Cited by 30 publications

References 20 publications

Proceedings of the 9th International Natural Language Generation conference

Proceedings of the 9th International Natural Language Generation conference

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Linguistic Considerations in Automatic Question Generation

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