A Tailored Pre-training Model For Task-oriented Dialog Generation

Gu Jing, Wu Qingyang, Wu Chongruo, Shi Weiyan, Yu Zhou. Arxiv 2020

[Paper]    
BERT Distillation Efficiency And Optimization GPT Model Architecture Pretraining Methods Training Techniques

The recent success of large pre-trained language models such as BERT and GPT-2 has suggested the effectiveness of incorporating language priors in downstream dialog generation tasks. However, the performance of pre-trained models on the dialog task is not as optimal as expected. In this paper, we propose a Pre-trained Role Alternating Language model (PRAL), designed specifically 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 history discount to improve pre-training performance. We introduce a task-oriented dialog pretraining dataset by cleaning 13 existing data sets. We test PRAL on three different downstream tasks. The results show that PRAL performs better or on par with state-of-the-art methods.

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