Distractor Generation for Multiple Choice Questions Using Learning to Rank
TL
TL;DR
This work investigates how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions and empirically studies feature-based and neural net based ranking models with experiments on the recently released SciQ dataset and the MCQL dataset.
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.
References32 items
1
Distractor Generation with Generative Adversarial Nets for Automatically Creating Fill-in-the-blank Questions
2
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
3
Multiple Choice Question Generation Utilizing An Ontology
4
Distractor Generation for Chinese Fill-in-the-blank Items
5