Weak Supervision Enhanced Generative Network for Question Generation
Table 1: Comparison with other methods on SQuAD dataset. We demonstrate automatic evaluation results on BLEU 1-4, ROUGE-L, METEOR metrics. The best performance for each column is highlighted in boldface. The WeGen without pre-training means the pipeline of Answer-Related Encoder and Transferred Interaction module are not used and the control gate is abandoned. Refer to Section 4.4 for more details.
['Model', 'BLEU 1', 'BLEU 2', 'BLEU 3', 'BLEU 4', 'ROUGE-L', 'METEOR']
The experimental results reveal a number of interesting points. The copy mechanism improve the results significantly. It uses attentive read from word embedding of sequence in encoder and selective read from location-aware hidden states to enhance the capability of decoder and proves effectiveness of the repeat pattern in human communication. Transformer structure performs badly and just achieves better results than Vanilla Seq2Seq. This suggests that the pure attention-based models are not sufficient for question generation and the local features of sequences and the variant semantic relations should be modelled more effectively.