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.

本文介绍了一种新的干扰项生成方案，利用多任务和负答案训练策略，有效地生成多个干扰项，并证明该模型可以显著提高现有干扰项生成方法的质量，生成的干扰项多样化且对于多项选择题具有强大的干扰能力。