Data Distillation for Controlling Specificity in Dialogue Generation
Table 1: Adversarial evaluation results for different greedy vs. sampling decoding strategies.
['Model', 'AdverSuc', 'machine-vs-random']
Again, we use Adversarial Evaluation for comparing purposes. We report AdverSuc and machine-vs-random proposed by \newcitekannan. machine-vs-random denotes the the accuracy of distinguishing between machine-generated responses and randomly sampled responses using a machine evaluator, trained in a way similar to the evaluator in AdverSuc. As can be seen, sampling all the time obtains the best score for AdverSuc, but also extremely low score for machine-vs-random accuracy, which indicates the inferiority of the always sampling strategy. The proposed stochastic greedy model perform better than always taking greedy actions as in greedy. This indicates that properly combining greedy search and sampling will potentially lead to better results.