TEXT Open Intent Recognition (TEXTOIR)

TEXTOIR is the first high-quality Text Open Intent Recognition platform. This repo contains a convenient toolkit with extensible interfaces, integrating a series of state-of-the-art algorithms of two tasks (open intent detection and open intent discovery). We also release the pipeline framework and the visualized platform in the repo TEXTOIR-DEMO.

Introduction

TEXTOIR aims to provide a convenience toolkit for researchers to reproduce the related text open classification and clustering methods. It contains two tasks, which are defined as open intent detection and open intent discovery. Open intent detection aims to identify n-class known intents, and detect one-class open intent. Open intent discovery aims to leverage limited prior knowledge of known intents to find fine-grained known and open intent-wise clusters. Related papers and codes are collected in our previous released reading list.

Open Intent Recognition:

Updates 🔥 🔥 🔥

Date	Announcements
12/2023	🎆 🎆 New paper and SOTA in Open Intent Discovery. Refer to the directory USNID for the codes. Read the paper -- A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery (Published in IEEE TKDE 2023).
04/2023	🎆 🎆 New paper and SOTA in Open Intent Detection. Refer to the directory DA-ADB for the codes. Read the paper -- Learning Discriminative Representations and Decision Boundaries for Open Intent Detection (Published in IEEE/ACM TASLP 2023).
09/2021	🎆 🎆 The first integrated and visualized platform for text Open Intent Recognition TEXTOIR has been released. Refer to the directory TEXTOIR-DEMO for the demo codes. Read our paper TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition (Published in ACL 2021).
05/2021	New paper and baselines DeepAligned in Open Intent Discovery have been released. Read our paper Discovering New Intents with Deep Aligned Clustering (Published in AAAI 2021).
05/2021	New paper and baselines ADB in Open Intent Detection have been released. Read our paper Deep Open Intent Classification with Adaptive Decision Boundary (Published in AAAI 2021).
05/2020	New paper and baselines CDAC+ in Open Intent Discovery have been released. Read our paper Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement (Published in AAAI 2020).
07/2019	New paper and baselines DeepUNK in Open Intent Detection have been released. Read our paper Deep Unknown Intent Detection with Margin Loss (Published in ACL 2019).

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We strongly recommend you to use our TEXTOIR toolkit, which has standard and unified interfaces (especially data setting) to obtain fair and persuable results on benchmark intent datasets!

Benchmark Datasets

Datasets	Source
BANKING	Paper
OOS / CLINC150	Paper
StackOverflow	Paper

Integrated Models

Open Intent Detection

Model Name	Source	Published
OpenMax*	Paper Code	CVPR 2016
MSP	Paper Code	ICLR 2017
DOC	Paper Code	EMNLP 2017
DeepUnk	Paper Code	ACL 2019
SEG	Paper Code	ACL 2020
ADB	Paper Code	AAAI 2021
(K+1)-way	Paper Code	ACL 2021
MDF	Paper Code	ACL 2021
ARPL*	Paper Code	IEEE TPAMI 2022
KNNCL	Paper Code	ACL 2022
DA-ADB	Paper Code	IEEE/ACM TASLP 2023

New Intent Discovery

Setting	Model Name	Source	Published
Unsupervised	KM	Paper	BSMSP 1967
Unsupervised	AG	Paper	PR 1978
Unsupervised	SAE-KM	Paper	JMLR 2010
Unsupervised	DEC	Paper Code	ICML 2016
Unsupervised	DCN	Paper Code	ICML 2017
Unsupervised	CC	Paper Code	AAAI 2021
Unsupervised	SCCL	Paper Code	NAACL 2021
Unsupervised	USNID	Paper Code	IEEE TKDE 2023
Semi-supervised	KCL*	Paper Code	ICLR 2018
Semi-supervised	MCL*	Paper Code	ICLR 2019
Semi-supervised	DTC*	Paper Code	ICCV 2019
Semi-supervised	CDAC+	Paper Code	AAAI 2020
Semi-supervised	DeepAligned	Paper Code	AAAI 2021
Semi-supervised	GCD	Paper Code	CVPR 2022
Semi-supervised	MTP-CLNN	Paper Code	ACL 2022
Semi-supervised	USNID	Paper Code	IEEE TKDE 2023

(* denotes the CV model replaced with the BERT backbone)

Quick Start

1. Use anaconda to create Python (version >= 3.6) environment

conda create --name textoir python=3.6
conda activate textoir

2. Install PyTorch (Cuda version 11.2)

conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch -c conda-forge  

3. Clone the TEXTOIR repository, and choose the task (Take open intent detection as an example).

git clone git@github.com:thuiar/TEXTOIR.git
cd TEXTOIR
cd open_intent_detection

4. Install related environmental dependencies

pip install -r requirements.txt

5. Run examples (Take ADB as an example)

sh examples/run_ADB.sh

• Note that if you cannot download the pre-trained model directly from HuggingFace transformers, you need to download it yourself. We provide the pre-trained bert model in the following link:
  Baidu Cloud Drive with code: v8tk

Extensibility

This toolkit is extensible and supports adding new methods, datasets, configurations, backbones, dataloaders, losses conveniently. More detailed information can be seen in the tutorials of the directories open_intent_detection and open_intent_discovery.

Citations

If this work is helpful, or you want to use the codes and results in this repo, please cite the following papers:

• TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
• Learning Discriminative Representations and Decision Boundaries for Open Intent Detection
• A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery

@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",
    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",
    pages = "167--174",
    year = "2021",
    url = "https://aclanthology.org/2021.acl-demo.20",
    doi = "10.18653/v1/2021.acl-demo.20",
}

@article{DA-ADB, 
    title = {Learning Discriminative Representations and Decision Boundaries for Open Intent Detection},  
    author = {Zhang, Hanlei and Xu, Hua and Zhao, Shaojie and Zhou, Qianrui}, 
    journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},  
    volume = {31},
    pages = {1611-1623},
    year = {2023}, 
    doi = {10.1109/TASLP.2023.3265203} 
} 

@ARTICLE{USNID,
  author={Zhang, Hanlei and Xu, Hua and Wang, Xin and Long, Fei and Gao, Kai},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery}, 
  year={2023},
  doi={10.1109/TKDE.2023.3340732}
} 

Contributors

Hanlei Zhang, Shaojie Zhao, Xin Wang, Ting-En Lin, Qianrui Zhou, Huisheng Mao.

Bugs or questions?

If you have any questions, please open issues and illustrate your problems as detailed as possible. If you want to integrate your method in our repo, please feel free to pull request!