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Found 5 papers, 3 papers with code
KNN-Contrastive Learning for Out-of-Domain Intent Classification
The Out-of-Domain (OOD) intent classification is a basic and challenging task for dialogue systems.
Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
Pretraining data of large language models composes multiple domains (e. g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome models.
Discovering New Intents Using Latent Variables
In this paper, starting from the intuition that discovering intents could be beneficial to the identification of the known intents, we propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables.
The Open-World Lottery Ticket Hypothesis for OOD Intent Classification
Most existing methods of Out-of-Domain (OOD) intent classification rely on extensive auxiliary OOD corpora or specific training paradigms.
Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator.
