# TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition

arXiv: https://arxiv.org/abs/2110.15063 · HTML: https://mlsanity.com/paper/2110.15063/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Learning to Discover Novel Visual Categories via Deep Transfer Clustering](https://mlsanity.com/paper/1908.09884/) (2019)
- [Joint Unsupervised Learning of Deep Representations and Image Clusters](https://mlsanity.com/paper/1604.03628/) (2016)
- [Learning to cluster in order to transfer across domains and tasks](https://mlsanity.com/paper/1711.10125/) (2017)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)
- [Multi-class Classification without Multi-class Labels](https://mlsanity.com/paper/1901.00544/) (2019)
- [Discovering New Intents with Deep Aligned Clustering](https://mlsanity.com/paper/2012.08987/) (2020)
- [Deep Unknown Intent Detection with Margin Loss](https://mlsanity.com/paper/1906.00434/) (2019)
- [Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement](https://mlsanity.com/paper/1911.08891/) (2019)
- [Deep Open Intent Classification with Adaptive Decision Boundary](https://mlsanity.com/paper/2012.10209/) (2020)
- [Dialog Intent Induction with Deep Multi-View Clustering](https://mlsanity.com/paper/1908.11487/) (2019)

## Built on by (most cited first, 24 of 24)

- [MIntRec: A New Dataset for Multimodal Intent Recognition](https://mlsanity.com/paper/2209.04355/) (2022)
- [Generalized Category Discovery with Decoupled Prototypical Network](https://mlsanity.com/paper/2211.15115/) (2022)
- [New Intent Discovery with Pre-training and Contrastive Learning](https://mlsanity.com/paper/2205.12914/) (2022)
- [Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems](https://mlsanity.com/paper/2205.15060/) (2022)
- [MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations](https://mlsanity.com/paper/2403.10943/) (2024)
- [IDAS: Intent Discovery with Abstractive Summarization](https://mlsanity.com/paper/2305.19783/) (2023)
- [Learning Discriminative Representations and Decision Boundaries for Open Intent Detection](https://mlsanity.com/paper/2203.05823/) (2022)
- [A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery](https://mlsanity.com/paper/2304.07699/) (2023)
- [ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection](https://mlsanity.com/paper/2308.13517/) (2023)
- [Redwood: Using Collision Detection to Grow a Large-Scale Intent Classification Dataset](https://mlsanity.com/paper/2204.05483/) (2022)
- [Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts](https://mlsanity.com/paper/2305.03237/) (2023)
- [A Survey of Ontology Expansion for Conversational Understanding](https://mlsanity.com/paper/2410.15019/) (2024)
- [Deep Learning Approaches for Multimodal Intent Recognition: A Survey](https://mlsanity.com/paper/2507.22934/) (2025)
- [Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances](https://mlsanity.com/paper/2405.12775/) (2024)
- [LANID: LLM-assisted New Intent Discovery](https://mlsanity.com/paper/2503.23740/) (2025)
- [Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification](https://mlsanity.com/paper/2405.19967/) (2024)
- [IPQA: A Benchmark for Core Intent Identification in Personalized Question Answering](https://mlsanity.com/paper/2510.23536/) (2025)
- [Building the Intent Landscape of Real-World Conversational Corpora with Extractive Question-Answering Transformers](https://mlsanity.com/paper/2208.12886/) (2022)
- [Open World Classification with Adaptive Negative Samples](https://mlsanity.com/paper/2303.05581/) (2023)
- [IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery](https://mlsanity.com/paper/2310.16761/) (2023)
- [Utilisation of open intent recognition models for customer support intent detection](https://mlsanity.com/paper/2307.16544/) (2023)
- [Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery](https://mlsanity.com/paper/2410.20219/) (2024)
- [DROID: Dual Representation for Out-of-Scope Intent Detection](https://mlsanity.com/paper/2510.14110/) (2025)
- [Ellipsoid-Based Decision Boundaries for Open Intent Classification](https://mlsanity.com/paper/2511.16685/) (2025)

## Abstract (arXiv)

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 (Toolkit code: https://github.com/thuiar/TEXTOIR), and designs a framework to implement a complete process to both identify known intents and discover open intents (Demo code: https://github.com/thuiar/TEXTOIR-DEMO).