OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken
  Language Understanding
Libo Qin Qiguang Chen Xiao Xu Yunlong Feng Wanxiang Che
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}.