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The recent success of large pre-trained language models such as BERT andGPT-2 has suggested the effectiveness of incorporating language priors indownstream dialog generation tasks. However, the performance of pre-trainedmodels on the dialog task is not as optimal as expected. In this paper, wepropose a Pre-trained Role Alternating Language model (PRAL), designedspecifically for task-oriented conversational systems. We adopted (Wu et al.,2019) that models two speakers separately. We also design several techniques,such as start position randomization, knowledge distillation, and historydiscount to improve pre-training performance. We introduce a task-orienteddialog pretraining dataset by cleaning 13 existing data sets. We test PRAL onthree different downstream tasks. The results show that PRAL performs better oron par with state-of-the-art methods.

A Tailored Pre-Training Model for Task-Oriented Dialog Generation