Descripción
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This paper describes RevUP which deals with automatically generating gap-fill questions. RevUP consists of 3 parts: Sentence Selection, Gap Selection & Multiple Choice Distractor Selection. To select topicallyimportant sentences from texts, we propose a novel sentence ranking method based on topic distributions obtained from topic models. To select gap-phrases from each selected sentence, we collected human annotations, using the Amazon Mechanical Turk, on the relative relevance of candidate gaps. This data is used to train a discriminative classifier to predict the relevance of gaps, achieving an accuracy of 81.0%. Finally, we propose a novel method to choose distractors that are semantically similar to the gap-phrase and have contextual fit to the gap-fill question. By crowdsourcing the evaluation of our method through the Amazon Mechanical Turk, we found that 94% of the distractors selected were good. RevUP fills the semantic gap left open by previous work in this area, and represents a significant step towards automatically generating quality tests for teachers and self-motivated learners. | |
Internacional
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Si |
Nombre congreso
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The 10th Workshop on Innovative Use of NLP for Building Educational Applications. NAACL 2015 Workshops |
Tipo de participación
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960 |
Lugar del congreso
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Denver, Colorado, USA |
Revisores
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Si |
ISBN o ISSN
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978-1-941643-35-8 |
DOI
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Fecha inicio congreso
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04/06/2015 |
Fecha fin congreso
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04/06/2015 |
Desde la página
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154 |
Hasta la página
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161 |
Título de las actas
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Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications |