@inproceedings{zhang-etal-2021-textoir,
title = "{TEXTOIR}: An Integrated and Visualized Platform for Text Open Intent Recognition",
author = "Zhang, Hanlei and
Li, Xiaoteng and
Xu, Hua and
Zhang, Panpan and
Zhao, Kang and
Gao, Kai",
editor = "Ji, Heng and
Park, Jong C. and
Xia, Rui",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-demo.20",
doi = "10.18653/v1/2021.acl-demo.20",
pages = "167--174",
abstract = "TEXTOIR is the first integrated and visualized platform for text open intent recognition. It is composed of two main modules: open intent detection and open intent discovery. Each module integrates most of the state-of-the-art algorithms and benchmark intent datasets. It also contains an overall framework connecting the two modules in a pipeline scheme. In addition, this platform has visualized tools for data and model management, training, evaluation and analysis of the performance from different aspects. TEXTOIR provides useful toolkits and convenient visualized interfaces for each sub-module, and designs a framework to implement a complete process to both identify known intents and discover open intents.",
}
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<abstract>TEXTOIR is the first integrated and visualized platform for text open intent recognition. It is composed of two main modules: open intent detection and open intent discovery. Each module integrates most of the state-of-the-art algorithms and benchmark intent datasets. It also contains an overall framework connecting the two modules in a pipeline scheme. In addition, this platform has visualized tools for data and model management, training, evaluation and analysis of the performance from different aspects. TEXTOIR provides useful toolkits and convenient visualized interfaces for each sub-module, and designs a framework to implement a complete process to both identify known intents and discover open intents.</abstract>
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%0 Conference Proceedings
%T TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
%A Zhang, Hanlei
%A Li, Xiaoteng
%A Xu, Hua
%A Zhang, Panpan
%A Zhao, Kang
%A Gao, Kai
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2021-textoir
%X TEXTOIR is the first integrated and visualized platform for text open intent recognition. It is composed of two main modules: open intent detection and open intent discovery. Each module integrates most of the state-of-the-art algorithms and benchmark intent datasets. It also contains an overall framework connecting the two modules in a pipeline scheme. In addition, this platform has visualized tools for data and model management, training, evaluation and analysis of the performance from different aspects. TEXTOIR provides useful toolkits and convenient visualized interfaces for each sub-module, and designs a framework to implement a complete process to both identify known intents and discover open intents.
%R 10.18653/v1/2021.acl-demo.20
%U https://aclanthology.org/2021.acl-demo.20
%U https://doi.org/10.18653/v1/2021.acl-demo.20
%P 167-174
Markdown (Informal)
[TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition](https://aclanthology.org/2021.acl-demo.20) (Zhang et al., ACL-IJCNLP 2021)
ACL
- Hanlei Zhang, Xiaoteng Li, Hua Xu, Panpan Zhang, Kang Zhao, and Kai Gao. 2021. TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 167–174, Online. Association for Computational Linguistics.