Question-type Driven Question Generation
Table 3: Experimental results of our model comparing with previous methods on two datasets.
['[BOLD] Dataset  [BOLD] Model', '[BOLD] SQuAD  [BOLD] BLEU-1', '[BOLD] SQuAD  [BOLD] BLEU-2', '[BOLD] SQuAD  [BOLD] BLEU-3', '[BOLD] SQuAD  [BOLD] BLEU-4', '[BOLD] MARCO  [BOLD] BLEU-1', '[BOLD] MARCO  [BOLD] BLEU-2', '[BOLD] MARCO  [BOLD] BLEU-3', '[BOLD] MARCO  [BOLD] BLEU-4']
Main Results By incorporating the question type prediction, our model obtains obvious performance gain over the baseline model, with 1.42 points gain on SQuAD and 1.44 on MARCO. Comparing with previous methods, our model outperforms the existing best methods, achieving new state-of-the-art results on both datasets, with 16.31 on SQuAD and 21.59 on MARCO.