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.
Libraries
Use these libraries to find Open Intent Detection models and implementationsMost 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
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
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
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
Open intent detection, a crucial aspect of natural language understanding, involves the identification of previously unseen intents in user-generated text.