论文标题
多域端到端以任务为导向的对话框的动态融合网络
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
论文作者
论文摘要
最近的研究表明，在端到端以任务为导向的对话系统中取得了显着成功。但是，大多数神经模型都依赖大型培训数据，这些数据仅适用于一定数量的任务域，例如导航和调度。 这使得对于具有有限标记数据的新域很难可扩展。但是，关于如何有效使用来自所有领域的数据来改善每个域和看不见的域的性能的研究相对较少。为此，我们研究可以明确使用域知识并引入共享私人网络以学习共享和特定知识的方法。此外，我们提出了一个新型的动态融合网络（DF-NET），该网络自动利用目标域与每个域之间的相关性。结果表明，我们的模型优于多域对话的现有方法，从而赋予了文献中最新的方法。此外，由于培训数据很少，我们通过平均表现优于先前的最佳模型13.9％来显示其可传递性。
Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a certain number of task domains, such as navigation and scheduling. This makes it difficult to scalable for a new domain with limited labeled data. However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains. To this end, we investigate methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge. In addition, we propose a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain. Results show that our model outperforms existing methods on multi-domain dialogue, giving the state-of-the-art in the literature. Besides, with little training data, we show its transferability by outperforming prior best model by 13.9\% on average.
