%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