Intent Discovery
16 papers with code • 3 benchmarks • 3 datasets
Given a set of labelled and unlabelled utterances, the idea is to identify existing (known) intents and potential (new intents) intents. This method can be utilised in conversational system setting.
Benchmarks
These leaderboards are used to track progress in Intent Discovery
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ATIS
SNIPS
Persian-ATIS
Most implemented papers
Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery
Previous methods suffer from a coupling of pseudo label disambiguation and representation learning, that is, the reliability of pseudo labels relies on representation learning, and representation learning is restricted by pseudo labels in turn.
IDAS: Intent Discovery with Abstractive Summarization
maarten-deraedt/idas-intent-discovery-with-abstract-summarization
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Intent discovery is the task of inferring latent intents from a set of unlabeled utterances, and is a useful step towards the efficient creation of new conversational agents.
Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition
In a practical dialogue system, users may input out-of-domain (OOD) queries.
Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT
The tasks of out-of-domain (OOD) intent discovery and generalized intent discovery (GID) aim to extend a closed intent classifier to open-world intent sets, which is crucial to task-oriented dialogue (TOD) systems.
A Diffusion Weighted Graph Framework for New Intent Discovery
yibai-shi/dwgf
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New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents.
