TY - GEN
T1 - Distractor generation for multiple choice questions using learning to rank
AU - Liang, Chen
AU - Yang, Xiao
AU - Dave, Neisarg
AU - Wham, Drew
AU - Pursel, Bart
AU - Giles, C. Lee
N1 - Funding Information:
We gratefully acknowledge partial support from the Pennsylvania State University Center for On-line Innovation in Learning and helpful comments from the reviewers.
Publisher Copyright:
© 2018 Association for Computational Linguistics
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85070093898&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85070093898
T3 - 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
SP - 284
EP - 290
BT - 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
A2 - Tetreault, Joel
A2 - Burstein, Jill
A2 - Kochmar, Ekaterina
A2 - Leacock, Claudia
A2 - Yannakoudakis, Helen
PB - Association for Computational Linguistics (ACL)
T2 - 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
Y2 - 5 June 2018
ER -