RevUP: Automatic gap-fill question generation from educational texts

Kumar, Girish; Banchs, Rafael E. y D'Haro Enriquez, Luis Fernando (2015). RevUP: Automatic gap-fill question generation from educational texts. En: "10th Workshop on Innovative Use of NLP for Building Educational Applications", 31/05/2015 - 05/06/2015, Denver, Colorado, USA. pp. 154-161.

Descripción

Título: RevUP: Automatic gap-fill question generation from educational texts
Autor/es:
  • Kumar, Girish
  • Banchs, Rafael E.
  • D'Haro Enriquez, Luis Fernando
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 10th Workshop on Innovative Use of NLP for Building Educational Applications
Fechas del Evento: 31/05/2015 - 05/06/2015
Lugar del Evento: Denver, Colorado, USA
Título del Libro: 10th Workshop on Innovative Use of NLP for Building Educational Applications
Fecha: 2015
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 42192
Identificador DC: http://oa.upm.es/42192/
Identificador OAI: oai:oa.upm.es:42192
URL Oficial: http://www.aclweb.org/anthology/W15-0618
Depositado por: Memoria Investigacion
Depositado el: 24 Jul 2016 08:21
Ultima Modificación: 24 Jul 2016 08:21
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