This thesis proposes a retrieve-and-generate strategy for task-oriented dialogue systems that combines the benefits of modular and end-to-end architectures. By incorporating syntax embeddings and leveraging entity-level metadata, the approach achieves improved performance on evaluation benchmarks and outperforms existing state-of-the-art models on the Entity-F1 metric.