O pen SLU : A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding (2023)

Abstract: 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 https://github.com/LightChen233/OpenSLU .

## introduction spoken language understanding (slu), which is used to extract the semantic frame of user queries (e.g., intents and slots) @xcite . typically, slu consists of two sub-tasks: intent detection and slot filling. take the utterance shown in figure 1 as an example, given "listen to rock music", the outputs include an intent class label (i.e., listen-to-music) and a slot label sequence (i.e., o, o, b-music-type, i-music-type). since intent detection and slot filling are highly tied @xcite , dominant methods in the literature explore joint models for slu to capture shared knowledge @xcite @xcite . recently, @xcite shows that, in the amazon internal dataset, 52% of examples contain multiple intents. inspired by this observation, various slu works shift their eye from single-intent slu to multi-intent slu scenario (gangadharaiah and narayanaswamy, 2019; @xcite @xcite . ## experiments extensive reproduction experiments are conducted to evaluate the effectiveness of openslu. ## conclusion this paper introduces openslu, a unified, modularized, and extensible toolkit for spoken language understanding. in our toolkit, we implement 10 models on both single-and multi-intent slu settings, both covering the categories of non-pretrained and pretrained language models. our toolkit can be easily applied to other slu settings, which is extensible to support seamless incorporation of other external modules. to the best of our knowledge, this is the first open-resource toolkit for slu and we hope openslu can attract more breakthroughs in slu. in the future, we can extend openslu to support cross-lingual @xcite and profile @xcite slu scenario.