C a TER : A Framework for Context-aware Topology Entity Retrieval Contrastive Learning in End-to-End Task-Oriented Dialogue Systems (2025)

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.

## introduction task-oriented dialogue (tod) systems must effectively handle localized and personalized tasks by retrieving knowledge about specific entities from large-scale knowledge base (kb). the rapid advancement of large language models @xcite such as deepseek @xcite and @xcite has introduced novel paradigms for tod systems in large-scale kb scenario, thereby significantly accelerating the development of tod technologies. one key limitation of current tod systems is the lack of belief state supervision, which traditionally guides accurate retrieval from external * corresponding author figure 1 : an example of entity knowledge retrieval. the retriever equally considers both highly relevant attributes (marked in red) and weakly relevant attributes (marked in green), inducing retrieval-generation bias and resulting in incorrect dialogue responses. kbs. existing methods can be broadly categorized into two paradigms. the first line of work @xcite integrates entity retrieval into response generation, enabling implicit knowledge retrieval by encoding kb facts into model parameters under reference-response supervision. the second line @xcite @xcite decouples retrieval and generation, using pseudo-supervision from generated responses to improve entity selection. for example, q-tod @xcite ) extracts key information from dialogue context and explicitly retrieves relevant entities before generating responses. while some approaches adopt semi-supervised training strategies (e.g., galaxy @xcite , labes @xcite , jsa-krtod @xcite )), we consider supervision level to be orthogonal to retrieval architecture: both paradigms can be trained in a fully-or semi-supervised way. the distractive attribution problem (dap) first formalized by real @xcite , which highlighted that false but similar knowledge, such as hard negative entities, can mislead generation models due to attribute distraction. real proposes a two-stage contrastive framework to alleviate dap through adaptive negative sampling and generatorguided retriever alignment. building upon this insight, we further analyze dap from a fine-grained modeling perspective, identifying three key error sources that jointly degrade tod performance, as illustrated in figure 1: (1)entity retrieval inaccuracy. tod systems are prone to errors when retrieving top-k entities from the kb, as the process is often affected by ambiguous entity attributes in the dialogue context. specifically, we define retrieval-generation knowledge transfer discrepancy as the misalignment between retrieved knowledge and the factual content grounded in the generated response. while the retriever may select entities whose attributes are superficially relevant, the generator may misattribute or blend information from incorrect entities due to high attribute similarity and insufficient grounding supervision. this discrepancy is distinct from retrieval inaccuracy (incorrect entities retrieved) and implicit association bias (generator favoring popular entities), as it arises after seemingly reasonable retrieval, but results in hallucinated or mixed-slot responses. a concrete example is discussed in 1, where cater retrieves the correct restaurant ("hakka") but generates an incorrect area information from a hard negative ("hk fusion"). the same phenomenon was happened in the failure case at appendix c. to address these issues, we propose a contextaware topology entity retrieval contrastive learning framework (cater), which introduces a cycle context-aware distilling attention (cycad) mechanism that performs contextindependent sparse entity pooling, allowing the generator to focus more on entity knowledge and reduce interference from weakly relevant attributes. additionally, we design a topology entity re-trieval contrastive learning method (ter) that constructs topologically hard negative samples during training and optimizes a topological contrastive loss to reduce generator bias toward head entities. extensive experiments on three standard tod benchmarks, multiwoz 2.1, camrest, and smd, demonstrate that cater consistently outperforms strong baselines in both dialogue-level and datasetlevel kb settings. our main contributions are as follows: ## the cater framework a novel cater framework is proposed, as illustrated in figure 2 . relevant entity attributes are masked through entity and attribute score. a cycad is introduced, which employs contextindependent sparse entity pooling to mask dialogue context information, guiding the generator to focus more on entity knowledge. a ter method is designed, which leverages cycad during training to construct topologically hard negative entity samples. response entity is decoupled from dialogue responses, and a topology entity contrastive loss is introduced. the retriever is then trained by reverse distillation. ## conclusion in this paper, we propose a novel context-aware topology entity retrieval contrastive learning framework (cater), which incorporates a cycle contextaware distilling attention mechanism to construct semantically correlated topological hard negative samples. a topology entity contrastive loss is then used to distill knowledge back into the retriever through reverse training. experimental results demonstrate that cater effectively captures latent relationships among kb entities and significantly improves the retriever's ability to select entities aligned with user intent in both dialogue-level and dataset-level kb settings. cater alleviates this issue by bridging the gap between retrieved entities and generated responses. overall, this work offers a new perspective and practical solution for enhancing entity retrieval and response generation in endto-end tod systems, with considerable practical value for generalization and real-world application.