Papers with CCN
Cross Copy Network for Dialogue Generation (2020.emnlp-main)
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| Challenge: | Despite the success of sequence-to-sequence models, dialogue logics are often ignored. |
| Approach: | They propose a network architecture to explore the current dialog context and similar dialogue instances’ logical structure simultaneously. |
| Outcome: | The proposed network architecture is superior to existing state-of-the-art models. |
Enhancing Multi-Label Text Classification under Label-Dependent Noise: A Label-Specific Denoising Framework (2024.findings-emnlp)
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| Challenge: | Existing noisy multi-label text classification methods rely on the class-conditional noise assumption, but in practice, noisy labels exhibit a certain degree of correlation with the true labels. |
| Approach: | They propose a label-specific denoising framework to counteract label-dependent noise by evaluating loss information, ranking information, and feature centroid. |
| Outcome: | The proposed framework significantly improves over existing state-of-the-art models under both synthetic and real-world noise conditions. |