Slot labelling is an essential component of any dialogue system, aiming to find important arguments in every user turn. Common approaches involve large pre-trained language models (PLMs) like BERT or RoBERTa, but they face challenges such as high computational requirements and dependence on pre-training data. In this work, we propose a lightweight method which performs on par or better than the state-of-the-art PLM-based methods, while having almost 10x less trainable parameters. This makes it especially applicable for real-life industry scenarios.

翻译：槽位标注是任何对话系统中的关键组成部分，旨在从每个用户交互轮次中提取重要参数。常见方法依赖于大型预训练语言模型（如BERT或RoBERTa），但这类方法面临计算资源需求高且依赖预训练数据等挑战。本文提出一种轻量级方法，其性能与基于预训练语言模型的先进方法持平或更优，而可训练参数数量减少近10倍，这使得该方法尤其适用于实际工业场景。