  Task-oriented dialogue systems have been a promising area in the NLP field.
Previous work showed the effectiveness of using a single GPT-2 based model to
predict belief states and responses via causal language modeling. In this
paper, we leverage multi-task learning techniques to train a GPT-2 based model
on a more challenging dataset with multiple domains, multiple modalities, and
more diversity in output formats.
  Using only a single model, our method achieves better performance on all
sub-tasks, across domains, compared to task and domain-specific models.
Furthermore, we evaluated several proposed strategies for GPT-2 based dialogue
systems with comprehensive ablation studies, showing that all techniques can
further improve the performance.
