CaTER: A Framework for Context-aware Topology Entity Retrieval Contrastive Learning in End-to-End Task-Oriented Dialogue Systems

Abstract

Retrieving entity knowledge that aligns with user intent is essential for task-oriented dialogue (TOD) systems to support personalization and localization, especially under large-scale knowledge bases. However, generative models tend to suffer from implicit association preference, while retrieval-generation approaches face knowledge transfer discrepancies. To address these challenges, we propose CaTER, a Context-aware Topology Entity Retrieval Contrastive Learning Framework. CaTER introduces a cycle context-aware distilling attention mechanism, which employs context-independent sparse pooling to suppress noise from weakly relevant attributes. We further construct topologically hard negative samples by decoupling entity information from generated responses and design a topology entity retrieval contrastive loss to train the retriever by reverse distillation. Extensive experiments on three standard TOD benchmarks with both small and large-scale knowledge bases show that CaTER consistently outperforms strong baselines such as MAKER and MK-TOD, achieving state-of-the-art performance in TOD system.