  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.
