Automatic Distractor Generation for Multiple Choice Questions in Standard Tests

Zhaopeng Qiu, Xian Wu, Wei Fan


Abstract
To assess knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners who did not master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor generation, which can benefit various standard tests in a wide range of domains. In this paper, we propose a question and answer guided distractor generation (EDGE) framework to automate distractor generation. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Passage Module apply gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Generator Module applies attention mechanism to control the level of plausibility. Experimental results on a large-scale public dataset demonstrate that our model significantly outperforms existing models and achieves a new state-of-the-art.
Anthology ID:
2020.coling-main.189
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2096–2106
Language:
URL:
https://aclanthology.org/2020.coling-main.189
DOI:
10.18653/v1/2020.coling-main.189
Bibkey:
Cite (ACL):
Zhaopeng Qiu, Xian Wu, and Wei Fan. 2020. Automatic Distractor Generation for Multiple Choice Questions in Standard Tests. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2096–2106, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests (Qiu et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.189.pdf
Data
RACE