Open Intent Detection
6 papers with code • 17 benchmarks • 3 datasets
Open intent detection aims to identify n-class known intents, and detect one-class open intent.
Benchmarks
These leaderboards are used to track progress in Open Intent Detection
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Libraries
Use these libraries to find Open Intent Detection models and implementations
Banking_CG
Most implemented papers
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
It is composed of two main modules: open intent detection and open intent discovery.
Deep Unknown Intent Detection with Margin Loss
With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance.
Deep Open Intent Classification with Adaptive Decision Boundary
thuiar/Adaptive-Decision-Boundary
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In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification.
Learning Discriminative Representations and Decision Boundaries for Open Intent Detection
thuiar/textoir
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To address these issues, this paper presents an original framework called DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection.
Metric Learning and Adaptive Boundary for Out-of-Domain Detection
tgargiani/adaptive-boundary
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Based on the open-world environment, we often encounter the situation that the training and test data are sampled from different distributions.
ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection
fangyihao/gptaug
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Open intent detection, a crucial aspect of natural language understanding, involves the identification of previously unseen intents in user-generated text.
