Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
Table 2: Main results. The numbers with * indicate that the improvement of our framework over all baselines is statistically significant with p<0.05 under t-test.
['Model', 'SMD BLEU', 'SMD F1', 'SMD Navigate F1', 'SMD Weather F1', 'SMD Calendar F1', 'Multi-WOZ 2.1 BLEU', 'Multi-WOZ 2.1 F1', 'Multi-WOZ 2.1 Restaurant F1', 'Multi-WOZ 2.1 Attraction F1', 'Multi-WOZ 2.1 Hotel F1']
Follow the prior work Eric et al. ; Madotto et al. The basic shared-private framework outperforms the best prior model GLMP in all the datasets. This indicates that the combination of domain-shared and domain-specific features can better enhance each domain performanc compared with only utilizing the implicit domain-shared features. 2) Our framework achieves the state-of-the-art performance on two multi-domain task-oriented dialog datasets, namely SMD and Multi-WOZ 2.1. On SMD dataset, our model has the highest BLEU compared with baselines, which shows that our framework can generate more fluent response. More importantly, our model outperforms GLMP by 2.0% overall, 3.3% in the Navigate domain, 1.1% in the Weather domain and 0.6% in Schedule domain on entity F1 metric, which indicates that considering relevance between target domain input and all domains is effective for enhancing performance of each domain. On Multi-Woz 2.1 dataset, the same trend of improvement has been witnessed, which further shows the effectiveness of our framework.