@inproceedings{qin-etal-2023-openslu,
title = "{O}pen{SLU}: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding",
author = "Qin, Libo and
Chen, Qiguang and
Xu, Xiao and
Feng, Yunlong and
Che, Wanxiang",
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.9",
doi = "10.18653/v1/2023.acl-demo.9",
pages = "95--102",
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 \url{https://github.com/LightChen233/OpenSLU}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding
%A Qin, Libo
%A Chen, Qiguang
%A Xu, Xiao
%A Feng, Yunlong
%A Che, Wanxiang
%Y Bollegala, Danushka
%Y Huang, Ruihong
%Y Ritter, Alan
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F qin-etal-2023-openslu
%X 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.
%R 10.18653/v1/2023.acl-demo.9
%U https://aclanthology.org/2023.acl-demo.9
%U https://doi.org/10.18653/v1/2023.acl-demo.9
%P 95-102
Markdown (Informal)
[OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding](https://aclanthology.org/2023.acl-demo.9) (Qin et al., ACL 2023)
ACL