TIGS: An Inference Algorithm for Text Infilling with Gradient Search
Table 2: Human evaluation resutls
['Methods', 'Dialog', 'Poetry', 'APRC']
We also conduct the human evaluation to further compare TIGS, BiRNN-BiBS, BiRNN-GSN, and Mask-Self-attn. Following the setting in \newcitezhu2019text, we collect generations of each of the four methods on 50 randomly-selected test instances. Then we launch a crowd-sourcing online study, asking 10 evaluators to rank the generations. The method with the best generation receives a score of 4, and the other three methods receive scores of 3, 2, and 1 according to the rank, respectively. We can see that TIGS consistently outperforms all baselines.