Text Infilling
Table 1: Results of varying mask rates and number of blanks. The upper part of the table is the results of mask_rate=30%, while the lower part is the results of mask_rate=50%.
['#Blanks', 'Metric', 'Template', 'Seq2Seq', 'GAN', 'Self-attn']
As expected, with increasing mask rate and #blanks, the model performance (BLEU and PPL) drops. We can see that seq2seq and GAN provide comparable performance, while the self-attention model consistently outperforms both under varying settings in terms of different metrics, showing the advantage of bi-directional global context modeling.