Multi-Task Learning with Language Modeling for Question Generation
Table 1: Experimental results of our model in different settings 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']
Results on BLEU The experimental results on BLEU Papineni et al. Our full model (w/ features + language modeling) significantly outperforms previous models and achieves state-of-the-art results on both datasets, with 16.23 BLEU-4 score on SQuAD and 20.88 on MARCO respectively.