Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots). In this work, we introduce OpenSLU, an open-source toolkit to provide a unified, modularized, and extensible toolkit for spoken language understanding. Specifically, OpenSLU unifies 10 SLU models for both single-intent and multi-intent scenarios, which support both non-pretrained and pretrained models simultaneously. Additionally, OpenSLU is highly modularized and extensible by decomposing the model architecture, inference, and learning process into reusable modules, which allows researchers to quickly set up SLU experiments with highly flexible configurations. OpenSLU is implemented based on PyTorch, and released at \url{https://github.com/LightChen233/OpenSLU}.

翻译：口语语言理解（Spoken Language Understanding, SLU）是任务导向型对话系统的核心组件之一，旨在提取用户查询的语义信息（如意图和槽位）。本文介绍OpenSLU——一个开源工具包，为口语语言理解提供统一化、模块化且可扩展的解决方案。具体而言，OpenSLU集成了10种SLU模型，同时支持单意图和多意图场景，并兼容非预训练与预训练模型。此外，通过将模型架构、推理及学习过程解耦为可复用模块，OpenSLU实现了高度模块化与可扩展性，使研究者能够通过高度灵活的配置快速搭建SLU实验。OpenSLU基于PyTorch实现，开源地址为\url{https://github.com/LightChen233/OpenSLU}。