Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems
Table 1: Evaluation on MultiWOZ with the greedy sampling procedure.
['[EMPTY]', 'Baseline', 'GPT', 'GPT2-S', 'GPT2-M']
First, three versions of GPT were fine-tuned on MultiWOZ and evaluated with greedy sampling. They show that the baseline obtains the highest score on task-related metrics while the highest BLUE score was achieved by GPT2-M. Although the results are lower for the GPT-based methods, we note the design simplicity of the GPT-based task-oriented dialogue models. Further, the gap in performance might be partially attributed to the chosen greedy sampling procedure which puts too much focus on the properties of the original pretraining phase Holtzman et al.