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：槽位标注是对话系统的关键组成部分，旨在从用户每轮对话中提取重要参数。常见方法依赖BERT或RoBERTa等大型预训练语言模型，但存在计算开销大、依赖预训练数据等挑战。本文提出一种轻量化方法，其性能与当前基于PLM的最优方法相当或更优，而可训练参数减少近10倍，尤其适用于现实工业场景。