Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog

ACL 2020  ·  Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang, Ting Liu ·

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.

PDF Abstract ACL 2020 PDF ACL 2020 Abstract


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Task-Oriented Dialogue Systems Kvret DF-Net Entity F1 62.7 # 1
Task-Oriented Dialogue Systems KVRET DF-Net Entity F1 62.5 # 3
BLEU 15.2 # 3


No methods listed for this paper. Add relevant methods here