@inproceedings{a19b6a72f8f84e9eadc690158cfb01e5,
title = "Distractor generation for multiple choice questions using learning to rank",
abstract = "We investigate how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions. Our proposed models can learn to select distractors that resemble those in actual exam questions, which is different from most existing unsupervised ontology-based and similarity-based methods. We empirically study feature-based and neural net (NN) based ranking models with experiments on the recently released SciQ dataset and our MCQL dataset. Experimental results show that feature-based ensemble learning methods (random forest and LambdaMART) outperform both the NN-based method and unsupervised baselines. These two datasets can also be used as benchmarks for distractor generation.",
author = "Chen Liang and Xiao Yang and Neisarg Dave and Drew Wham and Bart Pursel and Giles, {C. Lee}",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics; 13th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018 ; Conference date: 05-06-2018",
year = "2018",
language = "English (US)",
series = "Proceedings of the 13th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018",
publisher = "Association for Computational Linguistics (ACL)",
pages = "284--290",
editor = "Joel Tetreault and Jill Burstein and Ekaterina Kochmar and Claudia Leacock and Helen Yannakoudakis",
booktitle = "Proceedings of the 13th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics",
}