Distractor Generation Using Generative and Discriminative Capabilities of Transformer-based Models
Shiva Taslimipoor, Luca Benedetto, Mariano Felice, Paula Buttery
Abstract
Multiple Choice Questions (MCQs) are very common in both high-stakes and low-stakes examinations, and their effectiveness in assessing students relies on the quality and diversity of distractors, which are the incorrect answer options provided alongside the correct answer. Motivated by the progress in generative language models, we propose a two-step automatic distractor generation approach which is based on text to text transfer transformer models. Unlike most of previous methods for distractor generation, our approach does not rely on the correct answer options. Instead, it first generates both correct and incorrect answer options, and then discriminates potential correct options from distractors. Identified distractors are finally categorised based on semantic similarity scores into separate clusters, and the cluster heads are selected as our final distinct distractors. Experiments on two publicly available datasets show that our approach outperforms previous models both in the case of single-word answer options and longer-sequence reading comprehension questions.- Anthology ID:
- 2024.lrec-main.452
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 5052–5063
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.452
- DOI:
- Cite (ACL):
- Shiva Taslimipoor, Luca Benedetto, Mariano Felice, and Paula Buttery. 2024. Distractor Generation Using Generative and Discriminative Capabilities of Transformer-based Models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5052–5063, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Distractor Generation Using Generative and Discriminative Capabilities of Transformer-based Models (Taslimipoor et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.452.pdf