DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task
This work addresses noisy slot filling for dialogue systems, offering an incremental improvement over existing prompt-based methods.
The paper tackles the challenge of noisy slot filling in dialogue systems by proposing DemoNSF, a multi-task demonstration-based generative framework that introduces noisy auxiliary tasks and a noisy demonstration strategy, achieving strong generalization and outperforming baselines on two benchmarks.
Recently, prompt-based generative frameworks have shown impressive capabilities in sequence labeling tasks. However, in practical dialogue scenarios, relying solely on simplistic templates and traditional corpora presents a challenge for these methods in generalizing to unknown input perturbations. To address this gap, we propose a multi-task demonstration based generative framework for noisy slot filling, named DemoNSF. Specifically, we introduce three noisy auxiliary tasks, namely noisy recovery (NR), random mask (RM), and hybrid discrimination (HD), to implicitly capture semantic structural information of input perturbations at different granularities. In the downstream main task, we design a noisy demonstration construction strategy for the generative framework, which explicitly incorporates task-specific information and perturbed distribution during training and inference. Experiments on two benchmarks demonstrate that DemoNSF outperforms all baseline methods and achieves strong generalization. Further analysis provides empirical guidance for the practical application of generative frameworks. Our code is released at https://github.com/dongguanting/Demo-NSF.