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.
翻译:以任务为导向的对话系统一直是国家劳工政策领域的一个充满希望的领域。以前的工作表明,使用一个基于GPT-2的单一模型来预测信仰状态和通过因果语言模型作出反应是有效的。在本文中,我们利用多任务学习技术来培训基于GPT-2的模型,该模型基于一个更具挑战性的数据集,包括多个领域、多种模式和产出格式的多样化。我们的方法仅使用一个单一模型,与任务和特定领域模型相比,在各个领域的所有子任务上取得更好的业绩。此外,我们评估了几个基于GPT-2的对话系统的拟议战略,并进行了全面的通缩研究,表明所有技术都可以进一步改进业绩。