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 this https URL

我们提出了一个名为 DemoNSF 的嘈杂插槽填充的多任务示范生成框架，它引入了三个嘈杂辅助任务：嘈杂恢复 (NR)，随机遮罩 (RM) 和混合辨别 (HD)，以隐式地捕捉不同粒度输入扰动的语义结构信息。在下游主任务中，我们为生成框架设计了一个嘈杂示范构建策略，其在训练和推理期间明确地融入了任务特定信息和扰动分布。对两个基准测试的实验表明，DemoNSF 优于所有基准方法，并实现了强大的泛化能力。进一步的分析为生成框架的实际应用提供了经验指导。

DemoNSF：一个基于多任务演示的生成框架用于噪音槽填充任务

DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy  Slot Filling Task

Incomplete utterance rewriting has recently raised wide attention. However,
previous works do not consider the semantic structural information between
incomplete utterance and rewritten utterance or model the semantic structure
implicitly and insufficiently. To address this problem, we propose a
QUEry-Enhanced Network (QUEEN). Firstly, our proposed query template explicitly
brings guided semantic structural knowledge between the incomplete utterance
and the rewritten utterance making model perceive where to refer back to or
recover omitted tokens. Then, we adopt a fast and effective edit operation
scoring network to model the relation between two tokens. Benefiting from
proposed query template and the well-designed edit operation scoring network,
QUEEN achieves state-of-the-art performance on several public datasets.

提出了一个名为 QUEry-Enhanced Network（QUEEN）的模型，它使用查询模板和编辑操作评分网络来解决不完整话语重写的问题，实现了几个公共数据集上最先进的性能。