Learning Geometry-Aware Representations for New Intent Discovery (2024.acl-long)
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| Challenge: | Existing methods for intent classification fail to distinguish new intents due to intertwined centers . a novel framework that learns geometry-aware representations to maximally separate all intents is proposed . |
| Approach: | They propose a new intent discovery framework that learns geometry-aware representations to maximally separate all intents. |
| Outcome: | The proposed framework achieves a new state-of-the-art performance on three benchmarking datasets. |
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| Challenge: | Existing methods for detecting new intents with labeled data are not cluster-friendly . a robust prototypical attracting learning (RPAL) method is designed to compel instances to gravitate toward their corresponding prototype . |
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| Challenge: | Data annotation is expensive in Task-Oriented Dialogue systems. |
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| Challenge: | Existing approaches to intent detection rely on epoch wise clustering and classification based on labeled and unlabeled data. |
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ClusterPrompt: Cluster Semantic Enhanced Prompt Learning for New Intent Discovery (2023.findings-emnlp)
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| Challenge: | Existing methods for identifying new intent categories focus on relations between utterances and clusters, while neglecting the usage of semantics. |
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