Abstract
Distractor generation, which aims to generate the wrong option part of multi-choice questions, has been proposed to assist educators to test the examinees’ reading comprehension and reasoning ability. Recently, some Seq2Seq-based models have been proposed to solve the task of automatic distractor generation. However, they did not make full use of context information to generate distractors. In order to overcome this shortcoming, we propose a context reasoning attention network for distractor generation. Experimental results show that our model outperforms state-of-the-art baselines and improves the distractive ability of the generated distractors in terms of automatic evaluation and human evaluation.
M. Li and L. Wang—Equal contribution.
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Acknowledgment
This research is supported by National Natural Science Foundation of China (Grant No. 6201101015), Beijing Academy of Artificial Intelligence (BAAI), Natural Science Foundation of Guangdong Province (Grant No. 2021A1515012640), the Basic Research Fund of Shenzhen City (Grant No. JCYJ20210324120012033 and JCYJ20190813165003837), and Overseas Cooperation Research Fund of Tsinghua Shenzhen International Graduate School (Grant No. HW2021008).
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Li, M., Wang, L., Liu, H., Wang, W., Zheng, HT. (2022). Context Reasoning Attention Network: Generating Plausible Distractors for Multi-choice Questions. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13531. Springer, Cham. https://doi.org/10.1007/978-3-031-15934-3_49
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