Domain-specific Attention with Distributional Signatures for Multi-Domain End-to-end Task-Oriented Dialogue

The end-to-end task-oriented dialogue system has achieved great success in recent years. Most of these dialogue systems need to accommodate multi-domain dialogue in real-world scenarios. However, due to the high cost of dialogue data annotation and the scarcity of labeled dialogue data, existing methods are difficult to extend to new domains. Therefore, it is essential to use limited data to construct multi-domain dialogue systems. To solve this problem, we propose a novel domain attention module. It uses distributional signatures to construct a multi-domain dialogue system effectively with limited data, which has strong extensibility. We also define an adjacent n-gram pattern to explore potential patterns for dialogue entities. Experimental results show that our approach outperforms the baseline models on most metrics. In the few-shot scenario, we show our method gets a great improvement compared with previous methods while keeping a smaller model scale.