@inproceedings{ha-yaneva-2018-automatic,
title = "Automatic Distractor Suggestion for Multiple-Choice Tests Using Concept Embeddings and Information Retrieval",
author = "Ha, Le An and
Yaneva, Victoria",
editor = "Tetreault, Joel and
Burstein, Jill and
Kochmar, Ekaterina and
Leacock, Claudia and
Yannakoudakis, Helen",
booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0548",
doi = "10.18653/v1/W18-0548",
pages = "389--398",
abstract = "Developing plausible distractors (wrong answer options) when writing multiple-choice questions has been described as one of the most challenging and time-consuming parts of the item-writing process. In this paper we propose a fully automatic method for generating distractor suggestions for multiple-choice questions used in high-stakes medical exams. The system uses a question stem and the correct answer as an input and produces a list of suggested distractors ranked based on their similarity to the stem and the correct answer. To do this we use a novel approach of combining concept embeddings with information retrieval methods. We frame the evaluation as a prediction task where we aim to {``}predict{''} the human-produced distractors used in large sets of medical questions, i.e. if a distractor generated by our system is good enough it is likely to feature among the list of distractors produced by the human item-writers. The results reveal that combining concept embeddings with information retrieval approaches significantly improves the generation of plausible distractors and enables us to match around 1 in 5 of the human-produced distractors. The approach proposed in this paper is generalisable to all scenarios where the distractors refer to concepts.",
}
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<abstract>Developing plausible distractors (wrong answer options) when writing multiple-choice questions has been described as one of the most challenging and time-consuming parts of the item-writing process. In this paper we propose a fully automatic method for generating distractor suggestions for multiple-choice questions used in high-stakes medical exams. The system uses a question stem and the correct answer as an input and produces a list of suggested distractors ranked based on their similarity to the stem and the correct answer. To do this we use a novel approach of combining concept embeddings with information retrieval methods. We frame the evaluation as a prediction task where we aim to “predict” the human-produced distractors used in large sets of medical questions, i.e. if a distractor generated by our system is good enough it is likely to feature among the list of distractors produced by the human item-writers. The results reveal that combining concept embeddings with information retrieval approaches significantly improves the generation of plausible distractors and enables us to match around 1 in 5 of the human-produced distractors. The approach proposed in this paper is generalisable to all scenarios where the distractors refer to concepts.</abstract>
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%0 Conference Proceedings
%T Automatic Distractor Suggestion for Multiple-Choice Tests Using Concept Embeddings and Information Retrieval
%A Ha, Le An
%A Yaneva, Victoria
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F ha-yaneva-2018-automatic
%X Developing plausible distractors (wrong answer options) when writing multiple-choice questions has been described as one of the most challenging and time-consuming parts of the item-writing process. In this paper we propose a fully automatic method for generating distractor suggestions for multiple-choice questions used in high-stakes medical exams. The system uses a question stem and the correct answer as an input and produces a list of suggested distractors ranked based on their similarity to the stem and the correct answer. To do this we use a novel approach of combining concept embeddings with information retrieval methods. We frame the evaluation as a prediction task where we aim to “predict” the human-produced distractors used in large sets of medical questions, i.e. if a distractor generated by our system is good enough it is likely to feature among the list of distractors produced by the human item-writers. The results reveal that combining concept embeddings with information retrieval approaches significantly improves the generation of plausible distractors and enables us to match around 1 in 5 of the human-produced distractors. The approach proposed in this paper is generalisable to all scenarios where the distractors refer to concepts.
%R 10.18653/v1/W18-0548
%U https://aclanthology.org/W18-0548
%U https://doi.org/10.18653/v1/W18-0548
%P 389-398
Markdown (Informal)
[Automatic Distractor Suggestion for Multiple-Choice Tests Using Concept Embeddings and Information Retrieval](https://aclanthology.org/W18-0548) (Ha & Yaneva, BEA 2018)
ACL