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A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies
Ho-Lam Chung, Ying-Hong Chan, Yao-Chung Fan
In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from practical use. There is still room for DG quality improvement. Second, the existing DG designs are mainly for single distractor generation. However, for practical MCQ preparation, multiple distractors are desired. Aiming at these goals, in this paper, we present a new distractor generation scheme with multi-tasking and negative answer training strategies for effectively generating \textit{multiple} distractors. The experimental results show that (1) our model advances the state-of-the-art result from 28.65 to 39.81 (BLEU 1 score) and (2) the generated multiple distractors are diverse and show strong distracting power for multiple choice question.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Distractor Generation | RACE | BDG p.m. | BLEU-1 | 39.81 | #1 of 1 | Archive leaderboard | report |
| Distractor Generation | RACE | BDG p.m. | BLEU-2 | 24.81 | #1 of 1 | Archive leaderboard | report |
| Distractor Generation | RACE | BDG p.m. | BLEU-3 | 17.66 | #1 of 1 | Archive leaderboard | report |
| Distractor Generation | RACE | BDG p.m. | BLEU-4 | 13.56 | #1 of 1 | Archive leaderboard | report |
| Distractor Generation | RACE | BDG p.m. | ROUGE-L | 34.01 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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