Papers with Diffusion Weighted Graph Framework

1 papers
A Diffusion Weighted Graph Framework for New Intent Discovery (2023.emnlp-main)

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Challenge: Existing methods to learn from unlabeled data generate noisy supervisory signals . current methods only rely on semantic similarities to generate supervisory signal .
Approach: They propose a weighted DWGF framework to capture semantic similarities and structure relationships in data.
Outcome: The proposed method outperforms state-of-the-art models on evaluation metrics across multiple benchmark datasets.

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