NRKE: Noise-Removal of Knowledge-Enhanced Framework for Spoken Language Understanding

Integrating external knowledge with traditional spoken language understanding (SLU) models can effectively mitigate the ambiguity in user utterances in real-world scenarios. Knowledge graph, as a common source of external knowledge, encapsulates entities enriched with diverse attribute information. Nevertheless, existing models consider all entities as relevant, which introduces significant noise into the input. Additionally, not all attribute information of the entities is essential, resulting in considerable noise and redundancy. In this paper, we propose a Noise-Removal of Knowledge-Enhanced (NRKE) framework for SLU, which involves two different types of denoising. The first approach involves hard denoising via entity selection, where we leverage a small clean dataset and introduce a BERT-based auxiliary model to filter out entities unrelated to user utterances, effectively eliminating noisy entities. In addition, we further refine entity selection by incorporating Large Language Models (LLMs) to assist in filtering out entities unrelated to user utterances. The second method involves soft denoising through the selection of entity attribute information. This approach utilizes a keywords-based local semantic selection that gives greater weight to relevant local semantics associated with specific keywords. This allows us to capture task-related information from the chosen entities, thereby minimizing noise and redundancy. To evaluate the generalization capability of existing knowledge-enhanced SLU models, we construct a new dataset named KGCAIS. The experimental results show that our NRKE achieves better performance than the competing models on both the PROSLU and KGCAIS datasets.